A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Energy spectra unfolding of fast neutron sources using the group method of data handling and decision tree algorithms

NASA Astrophysics Data System (ADS)

Hosseini, Seyed Abolfazl; Afrakoti, Iman Esmaili Paeen

2017-04-01

Accurate unfolding of the energy spectrum of a neutron source gives important information about unknown neutron sources. The obtained information is useful in many areas like nuclear safeguards, nuclear nonproliferation, and homeland security. In the present study, the energy spectrum of a poly-energetic fast neutron source is reconstructed using the developed computational codes based on the Group Method of Data Handling (GMDH) and Decision Tree (DT) algorithms. The neutron pulse height distribution (neutron response function) in the considered NE-213 liquid organic scintillator has been simulated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). The developed computational codes based on the GMDH and DT algorithms use some data for training, testing and validation steps. In order to prepare the required data, 4000 randomly generated energy spectra distributed over 52 bins are used. The randomly generated energy spectra and the simulated neutron pulse height distributions by MCNPX-ESUT for each energy spectrum are used as the output and input data. Since there is no need to solve the inverse problem with an ill-conditioned response matrix, the unfolded energy spectrum has the highest accuracy. The 241Am-9Be and 252Cf neutron sources are used in the validation step of the calculation. The unfolded energy spectra for the used fast neutron sources have an excellent agreement with the reference ones. Also, the accuracy of the unfolded energy spectra obtained using the GMDH is slightly better than those obtained from the DT. The results obtained in the present study have good accuracy in comparison with the previously published paper based on the logsig and tansig transfer functions.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data.

PubMed

Wang, Kung-Jeng; Makond, Bunjira; Wang, Kung-Min

2013-11-09

Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE), cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

PubMed Central

2013-01-01

Background Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Methods Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Results Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. Conclusions LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR. PMID:24207108

Integrating Decision Tree and Hidden Markov Model (HMM) for Subtype Prediction of Human Influenza A Virus

NASA Astrophysics Data System (ADS)

Attaluri, Pavan K.; Chen, Zhengxin; Weerakoon, Aruna M.; Lu, Guoqing

Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at http://glee.ist.unomaha.edu/.

Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.

PubMed

Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi

2014-01-01

In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.

Decision-making for destination therapy left ventricular assist devices: implications for caregivers.

PubMed

McIlvennan, Colleen K; Jones, Jacqueline; Allen, Larry A; Lindenfeld, JoAnn; Swetz, Keith M; Nowels, Carolyn; Matlock, Daniel D

2015-03-01

Implanting centers often require the identification of a dedicated caregiver before destination therapy left ventricular assist device (DT LVAD) implantation; however, the caregiver experience surrounding this difficult decision is relatively unexplored. From October 2012 through July 2013, we conducted semistructured, in-depth interviews with caregivers of patients considering DT LVAD. Data were analyzed using a mixed inductive and deductive approach. We interviewed 17 caregivers: 10 caregivers of patients living with DT LVAD, 6 caregivers of patients who had died with DT LVAD, and 1 caregiver of a patient who had declined DT LVAD. The themes identified, which could also be considered dialectical tensions, are broadly interpreted under 3 domains mapping to decision context, process, and outcome: (1) the stark decision context, with tension between hope and reality; (2) the challenging decision process, with tension between wanting loved ones to live and wanting to respect loved ones' wishes; and (3) the downstream decision outcome, with tension between gratitude and burden. Decision-making surrounding DT LVAD should incorporate decision support for patients and caregivers. This should include a focus on caregiver burden and the predictable tensions that caregivers experience. © 2015 American Heart Association, Inc.

Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms.

PubMed

Jena, Manas Kumar; Samantaray, Subhransu Ranjan

2016-01-01

This paper presents a data-mining-based intelligent differential relaying scheme for transmission lines, including flexible ac transmission system device, such as unified power flow controller (UPFC) and wind farms. Initially, the current and voltage signals are processed through extended Kalman filter phasor measurement unit for phasor estimation, and 21 potential features are computed at both ends of the line. Once the features are extracted at both ends, the corresponding differential features are derived. These differential features are fed to a data-mining model known as decision tree (DT) to provide the final relaying decision. The proposed technique has been extensively tested for single-circuit transmission line, including UPFC and wind farms with in-feed, double-circuit line with UPFC on one line and wind farm as one of the substations with wide variations in operating parameters. The test results obtained from simulation as well as in real-time digital simulator testing indicate that the DT-based intelligent differential relaying scheme is highly reliable and accurate with a response time of 2.25 cycles from the fault inception.

Cost/efficacy analysis of preferred Spanish AIDS study group regimens and the dual therapy with lopinavir/ritonavir plus lamivudine for initial ART in HIV infected adults.

PubMed

Gatell Artigas, Josep María; Arribas López, José Ramón; Lázaro Y de Mercado, Pablo; Blasco Bravo, Antonio Javier

2016-01-01

The National AIDS Plan and the Spanish AIDS study group (GESIDA) proposes "preferred regimens" (PR) of antiretroviral treatment (ART) as initial therapy in HIV-infected patients. In 2013, the recommended regimens were all triple therapy regimens. The Gardel Study assessed the efficacy of a dual therapy (DT) combination of lopinavir/ritonavir (LPV/r) plus lamivudine (3TC). Our objective is to evaluate the GESIDA PR and the DT regimen LPV/r+3TC cost/efficacy ratios. Decision tree models were built. probability of having viral load <50 copies/mL at week 48. ART regime cost: costs of ART, adverse effects, and drug resistance tests during the first 48 weeks. Cost/efficacy ratios varied between 5,817 and 13,930 euros per responder at 48 weeks, for the DT of LPV/r+3TC and tenofovir DF/emtricitabine+raltegravir, respectively. Taking into account the official Spanish prices of ART, the most efficient regimen was DT of LPV/r+3TC, followed by the triple therapy with non-nucleoside containing regimens. Copyright © 2015 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Positively selected amino acid replacements within the RuBisCO enzyme of oak trees are associated with ecological adaptations.

PubMed

Hermida-Carrera, Carmen; Fares, Mario A; Fernández, Ángel; Gil-Pelegrín, Eustaquio; Kapralov, Maxim V; Mir, Arnau; Molins, Arántzazu; Peguero-Pina, José Javier; Rocha, Jairo; Sancho-Knapik, Domingo; Galmés, Jeroni

2017-01-01

Phylogenetic analysis by maximum likelihood (PAML) has become the standard approach to study positive selection at the molecular level, but other methods may provide complementary ways to identify amino acid replacements associated with particular conditions. Here, we compare results of the decision tree (DT) model method with ones of PAML using the key photosynthetic enzyme RuBisCO as a model system to study molecular adaptation to particular ecological conditions in oaks (Quercus). We sequenced the chloroplast rbcL gene encoding RuBisCO large subunit in 158 Quercus species, covering about a third of the global genus diversity. It has been hypothesized that RuBisCO has evolved differentially depending on the environmental conditions and leaf traits governing internal gas diffusion patterns. Here, we show, using PAML, that amino acid replacements at the residue positions 95, 145, 251, 262 and 328 of the RuBisCO large subunit have been the subject of positive selection along particular Quercus lineages associated with the leaf traits and climate characteristics. In parallel, the DT model identified amino acid replacements at sites 95, 219, 262 and 328 being associated with the leaf traits and climate characteristics, exhibiting partial overlap with the results obtained using PAML.

Positively selected amino acid replacements within the RuBisCO enzyme of oak trees are associated with ecological adaptations

PubMed Central

Hermida-Carrera, Carmen; Fares, Mario A.; Fernández, Ángel; Gil-Pelegrín, Eustaquio; Kapralov, Maxim V.; Mir, Arnau; Molins, Arántzazu; Peguero-Pina, José Javier; Rocha, Jairo; Sancho-Knapik, Domingo

2017-01-01

Phylogenetic analysis by maximum likelihood (PAML) has become the standard approach to study positive selection at the molecular level, but other methods may provide complementary ways to identify amino acid replacements associated with particular conditions. Here, we compare results of the decision tree (DT) model method with ones of PAML using the key photosynthetic enzyme RuBisCO as a model system to study molecular adaptation to particular ecological conditions in oaks (Quercus). We sequenced the chloroplast rbcL gene encoding RuBisCO large subunit in 158 Quercus species, covering about a third of the global genus diversity. It has been hypothesized that RuBisCO has evolved differentially depending on the environmental conditions and leaf traits governing internal gas diffusion patterns. Here, we show, using PAML, that amino acid replacements at the residue positions 95, 145, 251, 262 and 328 of the RuBisCO large subunit have been the subject of positive selection along particular Quercus lineages associated with the leaf traits and climate characteristics. In parallel, the DT model identified amino acid replacements at sites 95, 219, 262 and 328 being associated with the leaf traits and climate characteristics, exhibiting partial overlap with the results obtained using PAML. PMID:28859145

Distillation time alters essential oil yield, composition, and antioxidant activity of male Juniperus scopulorum trees.

PubMed

Zheljazkov, Valtcho D; Astatkie, Tess; Jeliazkova, Ekaterina A; Schlegel, Vicki

2012-01-01

The objective of this study was to evaluate the effect of 15 distillation times (DT), ranging from 1.25 to 960 min, on oil yield, essential oil profiles, and antioxidant capacity of male J. scopulorum trees. Essential oil yields were 0.07% at 1.25 min DT and reached a maximum of 1.48% at 840 min DT. The concentrations of alpha-thujene (1.76-2.75%), alpha-pinene (2.9-8.7%), sabinene (45-74.7%), myrcene (2.4-3.4%), and para-cymene (0.8-3.1%) were highest at the shortest DT (1.5 to 5 min) and decreased with increasing DT. Cis-sabinene hydrate (0.5-0.97%) and linalool plus trans-sabinene (0.56-1.6%) reached maximum levels at 40 min DT. Maximum concentrations of limonene (2.3-2.8%) and pregeijerene-B (0.06-1.4%) were obtained at 360-480 min DT, and 4-terpinenol (0.7-5.7%) at 480 min DT. Alpha-terpinene (0.16-2.9%), gamma-terpinene (0.3-4.9%) and terpinolene (0.3-1.4%) reached maximum at 720 min DT. The concentrations of delta-cadinene (0.06-1.65%), elemol (0-6.0%), and 8-alpha-acetoxyelemol (0-4.4%) reached maximum at 840 min DT. The yield of the essential oil constituents increased with increasing DT. Only linalool/transsabinene hydrate reached a maximum yield at 360 min DT. Maximum yields of the following constituents were obtained at 720 min DT: alpha-thujene, alpha-pinene, camphene, sabinene, myrcene, alpha-terpinene, para-cimene, limonene, gamma-terpinene, terpinolene, and 4-terpinenol. At 840 min DT, cis-sabinene hydrate, prejeijerene-B, gamma muurolene, delta-cadinene, reached maximum. At 960 min DT, maximum yields of beta-pinene, elemol, alphaeudesmol/betaeudesmol, 8-alpha-acetoxyelemol were reached. These changes were adequately modeled by either the Michaelis-Menten or the Power (Convex) nonlinear regression models. Oils from the 480 min DT showed higher antioxidant activity compared to samples collected at 40, 160, or 960 min DT. These results show the potential for obtaining essential oils with various compositions and antioxidant capacity from male J. scopulorum by varying DT. This study can be used as a reference paper for comparing results of reports where different lengths of the DT were used.

Assessment of various supervised learning algorithms using different performance metrics

NASA Astrophysics Data System (ADS)

Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

2017-11-01

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

Measuring exertion time, duty cycle and hand activity level for industrial tasks using computer vision.

PubMed

Akkas, Oguz; Lee, Cheng Hsien; Hu, Yu Hen; Harris Adamson, Carisa; Rempel, David; Radwin, Robert G

2017-12-01

Two computer vision algorithms were developed to automatically estimate exertion time, duty cycle (DC) and hand activity level (HAL) from videos of workers performing 50 industrial tasks. The average DC difference between manual frame-by-frame analysis and the computer vision DC was -5.8% for the Decision Tree (DT) algorithm, and 1.4% for the Feature Vector Training (FVT) algorithm. The average HAL difference was 0.5 for the DT algorithm and 0.3 for the FVT algorithm. A sensitivity analysis, conducted to examine the influence that deviations in DC have on HAL, found it remained unaffected when DC error was less than 5%. Thus, a DC error less than 10% will impact HAL less than 0.5 HAL, which is negligible. Automatic computer vision HAL estimates were therefore comparable to manual frame-by-frame estimates. Practitioner Summary: Computer vision was used to automatically estimate exertion time, duty cycle and hand activity level from videos of workers performing industrial tasks.

Return to Work After Lumbar Microdiscectomy - Personalizing Approach Through Predictive Modeling.

PubMed

Papić, Monika; Brdar, Sanja; Papić, Vladimir; Lončar-Turukalo, Tatjana

2016-01-01

Lumbar disc herniation (LDH) is the most common disease among working population requiring surgical intervention. This study aims to predict the return to work after operative treatment of LDH based on the observational study including 153 patients. The classification problem was approached using decision trees (DT), support vector machines (SVM) and multilayer perception (MLP) combined with RELIEF algorithm for feature selection. MLP provided best recall of 0.86 for the class of patients not returning to work, which combined with the selected features enables early identification and personalized targeted interventions towards subjects at risk of prolonged disability. The predictive modeling indicated at the most decisive risk factors in prolongation of work absence: psychosocial factors, mobility of the spine and structural changes of facet joints and professional factors including standing, sitting and microclimate.

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA

PubMed Central

Liu, Tieming; Shepherd, Scott; Paiva, William

2018-01-01

Objectives The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches. Methods This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined. Results Of 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96–3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18–1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.89, LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively. Conclusions The evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis. PMID:29770247

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study.

PubMed

Ramezankhani, Azra; Hadavandi, Esmaeil; Pournik, Omid; Shahrabi, Jamal; Azizi, Fereidoun; Hadaegh, Farzad

2016-12-01

The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Prospective cohort study. Tehran Lipid and Glucose Study (TLGS). A total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999-2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication. During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Our study emphasises the utility of DT for exploring interactions between predictor variables. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Towards sensible toxicity testing for nanomaterials: proposal for the specification of test design

NASA Astrophysics Data System (ADS)

Potthoff, Annegret; Weil, Mirco; Meißner, Tobias; Kühnel, Dana

2015-12-01

During the last decade, nanomaterials (NM) were extensively tested for potential harmful effects towards humans and environmental organisms. However, a sound hazard assessment was so far hampered by uncertainties and a low comparability of test results. The reason for the low comparability is a high variation in the (1) type of NM tested with regard to raw material, size and shape and (2) procedures before and during the toxicity testing. This calls for tailored, nanomaterial-specific protocols. Here, a structured approach is proposed, intended to lead to test protocols not only tailored to specific types of nanomaterials, but also to respective test system for toxicity testing. There are existing standards on single procedures involving nanomaterials, however, not all relevant procedures are covered by standards. Hence, our approach offers a detailed way of weighting several plausible alternatives for e.g. sample preparation, in order to decide on the procedure most meaningful for a specific nanomaterial and toxicity test. A framework of several decision trees (DT) and flow charts to support testing of NM is proposed as a basis for further refinement and in-depth elaboration. DT and flow charts were drafted for (1) general procedure—physicochemical characterisation, (2) choice of test media, (3) decision on test scenario and application of NM to liquid media, (4) application of NM to the gas phase, (5) application of NM to soil and sediments, (6) dose metrics, (S1) definition of a nanomaterial, and (S2) dissolution. The applicability of the proposed approach was surveyed by using experimental data retrieved from studies on nanoscale CuO. This survey demonstrated the DT and flow charts to be a convenient tool to systematically decide upon test procedures and processes, and hence pose an important step towards harmonisation of NM testing.

Towards sensible toxicity testing for nanomaterials: proposal for the specification of test design.

PubMed

Potthoff, Annegret; Weil, Mirco; Meißner, Tobias; Kühnel, Dana

2015-12-01

During the last decade, nanomaterials (NM) were extensively tested for potential harmful effects towards humans and environmental organisms. However, a sound hazard assessment was so far hampered by uncertainties and a low comparability of test results. The reason for the low comparability is a high variation in the (1) type of NM tested with regard to raw material, size and shape and (2) procedures before and during the toxicity testing. This calls for tailored, nanomaterial-specific protocols. Here, a structured approach is proposed, intended to lead to test protocols not only tailored to specific types of nanomaterials, but also to respective test system for toxicity testing. There are existing standards on single procedures involving nanomaterials, however, not all relevant procedures are covered by standards. Hence, our approach offers a detailed way of weighting several plausible alternatives for e.g. sample preparation, in order to decide on the procedure most meaningful for a specific nanomaterial and toxicity test. A framework of several decision trees (DT) and flow charts to support testing of NM is proposed as a basis for further refinement and in-depth elaboration. DT and flow charts were drafted for (1) general procedure-physicochemical characterisation, (2) choice of test media, (3) decision on test scenario and application of NM to liquid media, (4) application of NM to the gas phase, (5) application of NM to soil and sediments, (6) dose metrics, (S1) definition of a nanomaterial, and (S2) dissolution. The applicability of the proposed approach was surveyed by using experimental data retrieved from studies on nanoscale CuO. This survey demonstrated the DT and flow charts to be a convenient tool to systematically decide upon test procedures and processes, and hence pose an important step towards harmonisation of NM testing.

Decision Making for Destination Therapy Left Ventricular Assist Devices: “There was no choice” versus “I thought about it an awful lot”

PubMed Central

McIlvennan, Colleen K.; Allen, Larry A.; Nowels, Carolyn; Brieke, Andreas; Cleveland, Joseph C.; Matlock, Daniel D.

2014-01-01

Background Destination therapy left ventricular assist devices (DT LVAD) are one of the most invasive medical interventions for end-stage illness. How patients decide whether or not to proceed with device implantation is unknown. We aimed to understand the decision-making processes of patients who either accept or decline DT LVADs. Methods and Results Between October 2012–September 2013, we conducted semi-structured, in-depth interviews to understand patients’ decision-making experiences. Data were analyzed using a mixed inductive and deductive approach. Twenty-two eligible patients were interviewed, 15 with DT LVADs and 7 who declined. We found a strong dichotomy between decision processes with some patients (11 accepters) being “automatic” and others (3 accepters, 7 decliners) being “reflective” in their approach to decision making. The automatic group was characterized by a fear of dying and an overriding desire to live as long as possible: “[LVAD] was the only option I had…that or push up daisies…so I automatically took this”. In contrast, the reflective group went through a reasoned process of weighing risks, benefits, and burdens: “There are worse things than death.” Irrespective of approach, most patients experienced the DT LVAD decision as a highly emotional process and many sought support from their families or spiritually. Conclusion Some patients offered a DT LVAD face the decision by reflecting on a process and reasoning through risks and benefits. For others, the desire to live supersedes such reflective processing. Acknowledging this difference is important when considering how to support patients who are faced with this complex decision. PMID:24823949

Decision making for destination therapy left ventricular assist devices: "there was no choice" versus "I thought about it an awful lot".

PubMed

McIlvennan, Colleen K; Allen, Larry A; Nowels, Carolyn; Brieke, Andreas; Cleveland, Joseph C; Matlock, Daniel D

2014-05-01

Destination therapy left ventricular assist devices (DT LVADs) are one of the most invasive medical interventions for end-stage illness. How patients decide whether or not to proceed with device implantation is unknown. We aimed to understand the decision-making processes of patients who either accept or decline DT LVADs. Between October 2012 and September 2013, we conducted semistructured, in-depth interviews to understand patients' decision-making experiences. Data were analyzed using a mixed inductive and deductive approach. Twenty-two eligible patients were interviewed, 15 with DT LVADs and 7 who declined. We found a strong dichotomy between decision processes with some patients (11 accepters) being automatic and others (3 accepters, 7 decliners) being reflective in their approach to decision making. The automatic group was characterized by a fear of dying and an over-riding desire to live as long as possible: "[LVAD] was the only option I had…that or push up daisies…so I automatically took this." By contrast, the reflective group went through a reasoned process of weighing risks, benefits, and burdens: "There are worse things than death." Irrespective of approach, most patients experienced the DT LVAD decision as a highly emotional process and many sought support from their families or spiritually. Some patients offered a DT LVAD face the decision by reflecting on a process and reasoning through risks and benefits. For others, the desire to live supersedes such reflective processing. Acknowledging this difference is important when considering how to support patients who are faced with this complex decision. © 2014 American Heart Association, Inc.

Modelling daily water temperature from air temperature for the Missouri River.

PubMed

Zhu, Senlin; Nyarko, Emmanuel Karlo; Hadzima-Nyarko, Marijana

2018-01-01

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air-water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

Organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in Cyprinidae fish: Towards hints of their arrangements using advanced classification methods.

PubMed

Romanić, Snježana Herceg; Vuković, Gordana; Klinčić, Darija; Sarić, Marijana Matek; Župan, Ivan; Antanasijević, Davor; Popović, Aleksandar

2018-05-18

To tackle the ever-present global concern regarding human exposure to persistent organic pollutants (POPs) via food products, this study strived to indicate associations between organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in lake-fish tissue depending on the species and sampling season. Apart from the monitoring initiatives recommended in the Global Monitoring Plan for POPs, the study discussed 7 OCPs and 18 PCB congeners determined in three Cyprinidae species (rudd, carp, and Prussian carp) from Vransko Lake (Croatia), which are widely domesticated and reared as food fish across Europe and Asia. We exploit advanced classification algorithms, the Kohonen self-organizing maps (SOM) and Decision Trees (DT), to search for POP patterns typical for the investigated species. As indicated by SOM, some of the dioxin-like and non-dioxin-like PCBs (PCB-28, PCB-74, PCB-52, PCB-101, PCB-105, PCB-114, PCB-118, PCB-156 and PCB-157), α-HCH and β-HCH caused dissimilarities among fish species, but regardless of their weight and length. To support these suggestions, DT analysis sequenced the fish species and seasons based on the concentration of heavier congeners. The presented assumptions indicated that the supplemental application of SOM and DT offers advantageous features over the usually rough interpretation of POPs pattern and over the single use of the methods. Copyright © 2018 Elsevier Inc. All rights reserved.

A parallel decision tree-based method for user authentication based on keystroke patterns.

PubMed

Sheng, Yong; Phoha, Vir V; Rovnyak, Steven M

2005-08-01

We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%.

Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information

NASA Astrophysics Data System (ADS)

Mishra, Varun Narayan; Prasad, Rajendra; Kumar, Pradeep; Srivastava, Prashant K.; Rai, Praveen Kumar

2017-10-01

Updated and accurate information of rice-growing areas is vital for food security and investigating the environmental impact of rice ecosystems. The intent of this work is to explore the feasibility of dual-polarimetric C-band Radar Imaging Satellite-1 (RISAT-1) data in delineating rice crop fields from other land cover features. A two polarization combination of RISAT-1 backscatter, namely ratio (HH/HV) and difference (HH-HV), significantly enhanced the backscatter difference between rice and nonrice categories. With these inputs, a QUEST decision tree (DT) classifier is successfully employed to extract the spatial distribution of rice crop areas. The results showed the optimal polarization combination to be HH along with HH/HV and HH-HV for rice crop mapping with an accuracy of 88.57%. Results were further compared with a Landsat-8 operational land imager (OLI) optical sensor-derived rice crop map. Spatial agreement of almost 90% was achieved between outputs produced from Landsat-8 OLI and RISAT-1 data. The simplicity of the approach used in this work may serve as an effective tool for rice crop mapping.

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

PubMed Central

Ho, Wen-Hsien; Lee, King-Teh; Chen, Hong-Yaw; Ho, Te-Wei; Chiu, Herng-Chia

2012-01-01

Background A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection. PMID:22235270

Predicting large wildfires across western North America by modeling seasonal variation in soil water balance.

PubMed

Waring, Richard H; Coops, Nicholas C

A lengthening of the fire season, coupled with higher temperatures, increases the probability of fires throughout much of western North America. Although regional variation in the frequency of fires is well established, attempts to predict the occurrence of fire at a spatial resolution <10 km 2 have generally been unsuccessful. We hypothesized that predictions of fires might be improved if depletion of soil water reserves were coupled more directly to maximum leaf area index (LAI max ) and stomatal behavior. In an earlier publication, we used LAI max and a process-based forest growth model to derive and map the maximum available soil water storage capacity (ASW max ) of forested lands in western North America at l km resolution. To map large fires, we used data products acquired from NASA's Moderate Resolution Imaging Spectroradiometers (MODIS) over the period 2000-2009. To establish general relationships that incorporate the major biophysical processes that control evaporation and transpiration as well as the flammability of live and dead trees, we constructed a decision tree model (DT). We analyzed seasonal variation in the relative availability of soil water ( fASW ) for the years 2001, 2004, and 2007, representing respectively, low, moderate, and high rankings of areas burned. For these selected years, the DT predicted where forest fires >1 km occurred and did not occur at ~100,000 randomly located pixels with an average accuracy of 69 %. Extended over the decade, the area predicted burnt varied by as much as 50 %. The DT identified four seasonal combinations, most of which included exhaustion of ASW during the summer as critical; two combinations involving antecedent conditions the previous spring or fall accounted for 86 % of the predicted fires. The approach introduced in this paper can help identify forested areas where management efforts to reduce fire hazards might prove most beneficial.

Extensions and applications of ensemble-of-trees methods in machine learning

NASA Astrophysics Data System (ADS)

Bleich, Justin

Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of violence during probation hearings in court systems.

Beyond where to how: a machine learning approach for sensing mobility contexts using smartphone sensors.

PubMed

Guinness, Robert E

2015-04-28

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity.

Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors †

PubMed Central

Guinness, Robert E.

2015-01-01

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity. PMID:25928060

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

Perspectives from Mechanical Circulatory Support Coordinators on the Pre-Implantation Decision Process for Destination Therapy Left Ventricular Assist Devices

PubMed Central

McIlvennan, Colleen K.; Matlock, Daniel D.; Narayan, Madhav P.; Nowels, Carolyn; Thompson, Jocelyn S.; Cannon, Anne; Bradley, William J.; Allen, Larry A.

2015-01-01

Objective To understand mechanical circulatory support (MCS) coordinators’ perspectives related to destination therapy left ventricular assist devices (DT LVAD) decision making Background MCS coordinators are central to the team that interacts with patients considering DT LVAD, and are well positioned to comment upon the pre-implantation process. Methods From August 2012–January 2013, MCS coordinators were recruited to participate in semi-structured, in-depth interviews. Established qualitative approaches were used to analyze and interpret data. Results Eighteen MCS coordinators from 18 programs were interviewed. We found diversity in coordinators’ roles and high programmatic variability in how DT LVAD decisions are approached. Despite these differences, three themes were consistently recommended: 1) DT LVAD is a major patient-centered decision: “you’re your best advocate…this may not be the best choice for you”; 2) this decision benefits from an iterative, multidisciplinary process: “It is not a one-time conversation”; and 3) this process involves a tension between conveying enough detail about the process yet not overwhelming patients: “It’s sometimes hard to walk that line to not scare them but not paint a rainbow and butterflies picture.” Conclusions MCS coordinators endorsed a shared decision-making process that starts early, uses non-biased educational materials, and involves a multidisciplinary team sensitive to the tension between conveying enough detail about the therapy yet not overwhelming patients. PMID:25724116

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

PubMed

Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

2015-06-01

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

DT-CWT Robust Filtering Algorithm for The Extraction of Reference and Waviness from 3-D Nano Scalar Surfaces

NASA Astrophysics Data System (ADS)

Ren, Zhi Ying.; Gao, ChengHui.; Han, GuoQiang.; Ding, Shen; Lin, JianXing.

2014-04-01

Dual tree complex wavelet transform (DT-CWT) exhibits superiority of shift invariance, directional selectivity, perfect reconstruction (PR), and limited redundancy and can effectively separate various surface components. However, in nano scale the morphology contains pits and convexities and is more complex to characterize. This paper presents an improved approach which can simultaneously separate reference and waviness and allows an image to remain robust against abnormal signals. We included a bilateral filtering (BF) stage in DT-CWT to solve imaging problems. In order to verify the feasibility of the new method and to test its performance we used a computer simulation based on three generations of Wavelet and Improved DT-CWT and we conducted two case studies. Our results show that the improved DT-CWT not only enhances the robustness filtering under the conditions of abnormal interference, but also possesses accuracy and reliability of the reference and waviness from the 3-D nano scalar surfaces.

Differential diagnosis of pleural mesothelioma using Logic Learning Machine.

PubMed

Parodi, Stefano; Filiberti, Rosa; Marroni, Paola; Libener, Roberta; Ivaldi, Giovanni Paolo; Mussap, Michele; Ferrari, Enrico; Manneschi, Chiara; Montani, Erika; Muselli, Marco

2015-01-01

Tumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications. Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a "black-box" classification that does not provide biological information useful for clinical purposes. Logic Learning Machine (LLM) is an innovative method of supervised data analysis capable of building classifiers described by a set of intelligible rules including simple conditions in their antecedent part. It is essentially an efficient implementation of the Switching Neural Network model and reaches excellent classification accuracy while keeping low the computational demand. LLM was applied to data from a consecutive cohort of 169 patients admitted for diagnosis to two pulmonary departments in Northern Italy from 2009 to 2011. Patients included 52 malignant pleural mesotheliomas (MPM), 62 pleural metastases (MTX) from other tumours and 55 benign diseases (BD) associated with pleurisies. Concentration of three tumour markers (CEA, CYFRA 21-1 and SMRP) was measured in the pleural fluid of each patient and a cytological examination was also carried out. The performance of LLM and that of three competing methods (DT, KNN and ANN) was assessed by leave-one-out cross-validation. LLM outperformed all other considered methods. Global accuracy was 77.5% for LLM, 72.8% for DT, 54.4% for KNN, and 63.9% for ANN, respectively. In more details, LLM correctly classified 79% of MPM, 66% of MTX and 89% of BD. The corresponding figures for DT were: MPM = 83%, MTX = 55% and BD = 84%; for KNN: MPM = 58%, MTX = 45%, BD = 62%; for ANN: MPM = 71%, MTX = 47%, BD = 76%. Finally, LLM provided classification rules in a very good agreement with a priori knowledge about the biological role of the considered tumour markers. LLM is a new flexible tool potentially useful for the differential diagnosis of pleural mesothelioma.

Hierarchical classification of land use types using multiple vegetation indices to measure the effects of urbanization.

PubMed

Shishir, Sharmin; Tsuyuzaki, Shiro

2018-05-11

Detecting fine-scale spatiotemporal land use changes is a prerequisite for understanding and predicting the effects of urbanization and its related human impacts on the ecosystem. Land use changes are frequently examined using vegetation indices (VIs), although the validation of these indices has not been conducted at a high resolution. Therefore, a hierarchical classification was constructed to obtain accurate land use types at a fine scale. The characteristics of four popular VIs were investigated prior to examining the hierarchical classification by using Purbachal New Town, Bangladesh, which exhibits ongoing urbanization. These four VIs are the normalized difference VI (NDVI), green-red VI (GRVI), enhanced VI (EVI), and two-band EVI (EVI2). The reflectance data were obtained by the IKONOS (0.8-m resolution) and WorldView-2 sensor (0.5-m resolution) in 2001 and 2015, respectively. The hierarchical classification of land use types was constructed using a decision tree (DT) utilizing all four of the examined VIs. The accuracy of the classification was evaluated using ground truth data with multiple comparisons and kappa (κ) coefficients. The DT showed overall accuracies of 96.1 and 97.8% in 2001 and 2015, respectively, while the accuracies of the VIs were less than 91.2%. These results indicate that each VI exhibits unique advantages. In addition, the DT was the best classifier of land use types, particularly for native ecosystems represented by Shorea forests and homestead vegetation, at the fine scale. Since the conservation of these native ecosystems is of prime importance, DTs based on hierarchical classifications should be used more widely.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.

PubMed

Jian, Wushuai; Sun, Xueyan; Luo, Shuqian

2012-12-19

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

PubMed Central

2012-01-01

Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

PubMed

Liang, Zhaohui; Liu, Jun; Ou, Aihua; Zhang, Honglai; Li, Ziping; Huang, Jimmy Xiangji

2018-05-04

Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM. A two-step model is applied in our study. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. There are two data sets used in the experiments. One is a plain text data set indexed by medical experts. The other is a structured dataset on primary hypertension. The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The model performance is evaluated by the statistics of mean and variance, the average precision and coverage on the data sets. Two conventional shallow models (support vector machine / SVM and decision tree / DT) are applied as the comparisons to show the superiority of our proposed approach. The deep learning (DBN + SVM) model outperforms simple SVM and DT on two data sets in terms of all the evaluation measures, which confirms our motivation that the deep model is good at capturing the key features with less dependence when the index is built up by manpower. Our study shows the two-step deep learning model achieves high performance for medical information retrieval over the conventional shallow models. It is able to capture the features of both plain text and the highly-structured database of EMR data. The performance of the deep model is superior to the conventional shallow learning models such as SVM and DT. It is an appropriate knowledge-learning model for information retrieval of EMR system. Therefore, deep learning provides a good solution to improve the performance of CAMDM systems. Copyright © 2018. Published by Elsevier B.V.

Landslide Susceptibility Mapping of Tegucigalpa, Honduras Using Artificial Neural Network, Bayesian Network and Decision Trees

NASA Astrophysics Data System (ADS)

Garcia Urquia, E. L.; Braun, A.; Yamagishi, H.

2016-12-01

Tegucigalpa, the capital city of Honduras, experiences rainfall-induced landslides on a yearly basis. The high precipitation regime and the rugged topography the city has been built in couple with the lack of a proper urban expansion plan to contribute to the occurrence of landslides during the rainy season. Thousands of inhabitants live at risk of losing their belongings due to the construction of precarious shelters in landslide-prone areas on mountainous terrains and next to the riverbanks. Therefore, the city is in the need for landslide susceptibility and hazard maps to aid in the regulation of future development. Major challenges in the context of highly dynamic urbanizing areas are the overlap of natural and anthropogenic slope destabilizing factors, as well as the availability and accuracy of data. Data-driven multivariate techniques have proven to be powerful in discovering interrelations between factors, identifying important factors in large datasets, capturing non-linear problems and coping with noisy and incomplete data. This analysis focuses on the creation of a landslide susceptibility map using different methods from the field of data mining, Artificial Neural Networks (ANN), Bayesian Networks (BN) and Decision Trees (DT). The input dataset of the study contains geomorphological and hydrological factors derived from a digital elevation model with a 10 m resolution, lithological factors derived from a geological map, and anthropogenic factors, such as information on the development stage of the neighborhoods in Tegucigalpa and road density. Moreover, a landslide inventory map that was developed in 2014 through aerial photo interpretation was used as target variable in the analysis. The analysis covers an area of roughly 100 km2, while 8.95 km2 are occupied by landslides. In a first step, the dataset was explored by assessing and improving the data quality, identifying unimportant variables and finding interrelations. Then, based on a training partition of the dataset, the ANN, BN and DT were optimized for the prediction of landslides. The predictive power and ability to generalize of the resulting models were assessed in a test partition and evaluated using success rate curves, skill scores and by ensuring the spatial plausibility of the prediction.

The Importance of Tree Size and Fecundity for Wind Dispersal of Big-Leaf Mahogany

PubMed Central

Norghauer, Julian M.; Nock, Charles A.; Grogan, James

2011-01-01

Seed dispersal by wind is a critical yet poorly understood process in tropical forest trees. How tree size and fecundity affect this process at the population level remains largely unknown because of insufficient replication across adults. We measured seed dispersal by the endangered neotropical timber species big-leaf mahogany (Swietenia macrophylla King, Meliaceae) in the Brazilian Amazon at 25 relatively isolated trees using multiple 1-m wide belt transects extended 100 m downwind. Tree diameter and fecundity correlated positively with increased seed shadow extent; but in combination large, high fecundity trees contributed disproportionately to longer-distance dispersal events (>60 m). Among three empirical models fitted to seed density vs. distance in one dimension, the Student-t (2Dt) generally fit best (compared to the negative exponential and inverse power). When seedfall downwind was modelled in two dimensions using a normalised sample, it peaked furthest downwind (c. 25 m) for large, high-fecundity trees; with the inverse Gaussian and Weibull functions providing comparable fits that were slightly better than the lognormal. Although most seeds fell within 30 m of parent trees, relatively few juveniles were found within this distance, resulting in juvenile-to-seed ratios peaking at c. 35–45 m. Using the 2Dt model fits to predict seed densities downwind, coupled with known fecundity data for 2000–2009, we evaluated potential Swietenia regeneration near adults (≤30 m dispersal) and beyond 30 m. Mean seed arrival into canopy gaps >30 m downwind was more than 3× greater for large, high fecundity trees than small, high-fecundity trees. Tree seed production did not necessarily scale up proportionately with diameter, and was not consistent across years, and this resulting intraspecific variation can have important consequences for local patterns of dispersal in forests. Our results have important implications for management and conservation of big-leaf mahogany populations, and may apply to other threatened wind-dispersed Meliaceae trees. PMID:21408184

Global Genomic Epidemiology of Salmonella enterica Serovar Typhimurium DT104

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leekitcharoenphon, Pimlapas; Hendriksen, Rene S.; Le Hello, Simon

It has been 30 years since the initial emergence and subsequent rapid global spread of multidrug-resistant Salmonella enterica serovar Typhimurium DT104 (MDR DT104). Nonetheless, its origin and transmission route have never been revealed. In this paper, we used whole-genome sequencing (WGS) and temporally structured sequence analysis within a Bayesian framework to reconstruct temporal and spatial phylogenetic trees and estimate the rates of mutation and divergence times of 315 S. Typhimurium DT104 isolates sampled from 1969 to 2012 from 21 countries on six continents. DT104 was estimated to have emerged initially as antimicrobial susceptible in ~1948 (95% credible interval [CI], 1934more » to 1962) and later became MDR DT104 in ~1972 (95% CI, 1972 to 1988) through horizontal transfer of the 13-kb Salmonella genomic island 1 (SGI1) MDR region into susceptible strains already containing SGI1. This was followed by multiple transmission events, initially from central Europe and later between several European countries. An independent transmission to the United States and another to Japan occurred, and from there MDR DT104 was probably transmitted to Taiwan and Canada. An independent acquisition of resistance genes took place in Thailand in ~1975 (95% CI, 1975 to 1990). In Denmark, WGS analysis provided evidence for transmission of the organism between herds of animals. Interestingly, the demographic history of Danish MDR DT104 provided evidence for the success of the program to eradicate Salmonella from pig herds in Denmark from 1996 to 2000. Finally, the results from this study refute several hypotheses on the evolution of DT104 and suggest that WGS may be useful in monitoring emerging clones and devising strategies for prevention of Salmonella infections.« less

Global Genomic Epidemiology of Salmonella enterica Serovar Typhimurium DT104

PubMed Central

Hendriksen, Rene S.; Le Hello, Simon; Weill, François-Xavier; Baggesen, Dorte Lau; Jun, Se-Ran; Lund, Ole; Crook, Derrick W.; Wilson, Daniel J.; Aarestrup, Frank M.

2016-01-01

It has been 30 years since the initial emergence and subsequent rapid global spread of multidrug-resistant Salmonella enterica serovar Typhimurium DT104 (MDR DT104). Nonetheless, its origin and transmission route have never been revealed. We used whole-genome sequencing (WGS) and temporally structured sequence analysis within a Bayesian framework to reconstruct temporal and spatial phylogenetic trees and estimate the rates of mutation and divergence times of 315 S. Typhimurium DT104 isolates sampled from 1969 to 2012 from 21 countries on six continents. DT104 was estimated to have emerged initially as antimicrobial susceptible in ∼1948 (95% credible interval [CI], 1934 to 1962) and later became MDR DT104 in ∼1972 (95% CI, 1972 to 1988) through horizontal transfer of the 13-kb Salmonella genomic island 1 (SGI1) MDR region into susceptible strains already containing SGI1. This was followed by multiple transmission events, initially from central Europe and later between several European countries. An independent transmission to the United States and another to Japan occurred, and from there MDR DT104 was probably transmitted to Taiwan and Canada. An independent acquisition of resistance genes took place in Thailand in ∼1975 (95% CI, 1975 to 1990). In Denmark, WGS analysis provided evidence for transmission of the organism between herds of animals. Interestingly, the demographic history of Danish MDR DT104 provided evidence for the success of the program to eradicate Salmonella from pig herds in Denmark from 1996 to 2000. The results from this study refute several hypotheses on the evolution of DT104 and suggest that WGS may be useful in monitoring emerging clones and devising strategies for prevention of Salmonella infections. PMID:26944846

Global Genomic Epidemiology of Salmonella enterica Serovar Typhimurium DT104

DOE PAGES

Leekitcharoenphon, Pimlapas; Hendriksen, Rene S.; Le Hello, Simon; ...

2016-03-04

It has been 30 years since the initial emergence and subsequent rapid global spread of multidrug-resistant Salmonella enterica serovar Typhimurium DT104 (MDR DT104). Nonetheless, its origin and transmission route have never been revealed. In this paper, we used whole-genome sequencing (WGS) and temporally structured sequence analysis within a Bayesian framework to reconstruct temporal and spatial phylogenetic trees and estimate the rates of mutation and divergence times of 315 S. Typhimurium DT104 isolates sampled from 1969 to 2012 from 21 countries on six continents. DT104 was estimated to have emerged initially as antimicrobial susceptible in ~1948 (95% credible interval [CI], 1934more » to 1962) and later became MDR DT104 in ~1972 (95% CI, 1972 to 1988) through horizontal transfer of the 13-kb Salmonella genomic island 1 (SGI1) MDR region into susceptible strains already containing SGI1. This was followed by multiple transmission events, initially from central Europe and later between several European countries. An independent transmission to the United States and another to Japan occurred, and from there MDR DT104 was probably transmitted to Taiwan and Canada. An independent acquisition of resistance genes took place in Thailand in ~1975 (95% CI, 1975 to 1990). In Denmark, WGS analysis provided evidence for transmission of the organism between herds of animals. Interestingly, the demographic history of Danish MDR DT104 provided evidence for the success of the program to eradicate Salmonella from pig herds in Denmark from 1996 to 2000. Finally, the results from this study refute several hypotheses on the evolution of DT104 and suggest that WGS may be useful in monitoring emerging clones and devising strategies for prevention of Salmonella infections.« less

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San

2017-04-01

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-sensor image fusion algorithm based on multi-objective particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Xie, Xia-zhu; Xu, Ya-wei

2017-11-01

On the basis of DT-CWT (Dual-Tree Complex Wavelet Transform - DT-CWT) theory, an approach based on MOPSO (Multi-objective Particle Swarm Optimization Algorithm) was proposed to objectively choose the fused weights of low frequency sub-bands. High and low frequency sub-bands were produced by DT-CWT. Absolute value of coefficients was adopted as fusion rule to fuse high frequency sub-bands. Fusion weights in low frequency sub-bands were used as particles in MOPSO. Spatial Frequency and Average Gradient were adopted as two kinds of fitness functions in MOPSO. The experimental result shows that the proposed approach performances better than Average Fusion and fusion methods based on local variance and local energy respectively in brightness, clarity and quantitative evaluation which includes Entropy, Spatial Frequency, Average Gradient and QAB/F.

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

NASA Astrophysics Data System (ADS)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

The drosomycin multigene family: three-disulfide variants from Drosophila takahashii possess antibacterial activity

PubMed Central

Gao, Bin; Zhu, Shunyi

2016-01-01

Drosomycin (DRS) is a strictly antifungal peptide in Drosophila melanogaster, which contains four disulfide bridges (DBs) with three buried in molecular interior and one exposed on molecular surface to tie the amino- and carboxyl-termini of the molecule together (called wrapper disulfide bridge, WDB). Based on computational analysis of genomes of Drosophila species belonging to the Oriental lineage, we identified a new multigene family of DRS in Drosphila takahashii that includes a total of 11 DRS-encoding genes (termed DtDRS-1 to DtDRS-11) and a pseudogene. Phylogenetic tree and synteny analyses reveal orthologous relationship between DtDRSs and DRSs, indicating that orthologous genes of DRS-1, DRS-2, DRS-3 and DRS-6 have undergone duplication in D. takahashii and three amplifications (DtDRS-9 to DtDRS-11) of DRS-3 have lost WDB. Among the 11 genes, five are transcriptionally active in adult fruitflies. The ortholog of DRS (DtDRS-1) shows high structural and functional similarity to DRS while two WDB-deficient members display antibacterial activity accompanying complete loss or remarkable reduction of antifungal activity. To the best of our knowledge, this is the first report on the presence of three-disulfide antibacterial DRSs in a specific Drosophila species, suggesting a potential role of DB loss in neofunctionalization of a protein via structural adjustment. PMID:27562645

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Comparison modeling for alpine vegetation distribution in an arid area.

PubMed

Zhou, Jihua; Lai, Liming; Guan, Tianyu; Cai, Wetao; Gao, Nannan; Zhang, Xiaolong; Yang, Dawen; Cong, Zhentao; Zheng, Yuanrun

2016-07-01

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.

Models for H₃ receptor antagonist activity of sulfonylurea derivatives.

PubMed

Khatri, Naveen; Madan, A K

2014-03-01

The histamine H₃ receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H₃ receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H₃ receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H₃ receptor antagonist sulfonylurea derivatives. Copyright © 2013 Elsevier Inc. All rights reserved.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.

PubMed

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J; Ibarra-Manzano, Mario A

2016-03-05

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

PubMed Central

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R.; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J.; Ibarra-Manzano, Mario A.

2016-01-01

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. PMID:26959029

The Precautionary Principle, Evidence-Based Medicine, and Decision Theory in Public Health Evaluation

PubMed Central

Fischer, Alastair J.; Ghelardi, Gemma

2016-01-01

The precautionary principle (PP) has been used in the evaluation of the effectiveness and/or cost-effectiveness of interventions designed to prevent future harms in a range of activities, particularly in the area of the environment. Here, we provide details of circumstances under which the PP can be applied to the topic of harm reduction in Public Health. The definition of PP that we use says that the PP reverses the onus of proof of effectiveness between an intervention and its comparator when the intervention has been designed to reduce harm. We first describe the two frameworks used for health-care evaluation: evidence-based medicine (EBM) and decision theory (DT). EBM is usually used in treatment effectiveness evaluation, while either EBM or DT may be used in evaluating the effectiveness of the prevention of illness. For cost-effectiveness, DT is always used. The expectation in Public Health is that interventions employed to reduce harm will not actually increase harm, where “harm” in this context does not include opportunity cost. That implies that an intervention’s effectiveness can often be assumed. Attention should therefore focus on its cost-effectiveness. This view is consistent with the conclusions of DT. It is also very close to the PP notion of reversing the onus of proof, but is not consistent with EBM as normally practiced, where the onus is on showing a new practice to be superior to usual practice with a sufficiently high degree of certainty. Under our definitions, we show that where DT and the PP differ in their evaluation is in cost-effectiveness, but only for decisions that involve potential catastrophic circumstances, where the nation-state will act as if it is risk-averse. In those cases, it is likely that the state will pay more, and possibly much more, than DT would allow, in an attempt to mitigate impending disaster. That is, the rules that until now have governed all cost-effectiveness analyses are shown not to apply to catastrophic situations, where the PP applies. PMID:27458575

The Precautionary Principle, Evidence-Based Medicine, and Decision Theory in Public Health Evaluation.

PubMed

Fischer, Alastair J; Ghelardi, Gemma

2016-01-01

The precautionary principle (PP) has been used in the evaluation of the effectiveness and/or cost-effectiveness of interventions designed to prevent future harms in a range of activities, particularly in the area of the environment. Here, we provide details of circumstances under which the PP can be applied to the topic of harm reduction in Public Health. The definition of PP that we use says that the PP reverses the onus of proof of effectiveness between an intervention and its comparator when the intervention has been designed to reduce harm. We first describe the two frameworks used for health-care evaluation: evidence-based medicine (EBM) and decision theory (DT). EBM is usually used in treatment effectiveness evaluation, while either EBM or DT may be used in evaluating the effectiveness of the prevention of illness. For cost-effectiveness, DT is always used. The expectation in Public Health is that interventions employed to reduce harm will not actually increase harm, where "harm" in this context does not include opportunity cost. That implies that an intervention's effectiveness can often be assumed. Attention should therefore focus on its cost-effectiveness. This view is consistent with the conclusions of DT. It is also very close to the PP notion of reversing the onus of proof, but is not consistent with EBM as normally practiced, where the onus is on showing a new practice to be superior to usual practice with a sufficiently high degree of certainty. Under our definitions, we show that where DT and the PP differ in their evaluation is in cost-effectiveness, but only for decisions that involve potential catastrophic circumstances, where the nation-state will act as if it is risk-averse. In those cases, it is likely that the state will pay more, and possibly much more, than DT would allow, in an attempt to mitigate impending disaster. That is, the rules that until now have governed all cost-effectiveness analyses are shown not to apply to catastrophic situations, where the PP applies.

Accurate reconstruction in digital holographic microscopy using Fresnel dual-tree complex wavelet transform

NASA Astrophysics Data System (ADS)

Zhang, Xiaolei; Zhang, Xiangchao; Yuan, He; Zhang, Hao; Xu, Min

2018-02-01

Digital holography is a promising measurement method in the fields of bio-medicine and micro-electronics. But the captured images of digital holography are severely polluted by the speckle noise because of optical scattering and diffraction. Via analyzing the properties of Fresnel diffraction and the topographies of micro-structures, a novel reconstruction method based on the dual-tree complex wavelet transform (DT-CWT) is proposed. This algorithm is shiftinvariant and capable of obtaining sparse representations for the diffracted signals of salient features, thus it is well suited for multiresolution processing of the interferometric holograms of directional morphologies. An explicit representation of orthogonal Fresnel DT-CWT bases and a specific filtering method are developed. This method can effectively remove the speckle noise without destroying the salient features. Finally, the proposed reconstruction method is compared with the conventional Fresnel diffraction integration and Fresnel wavelet transform with compressive sensing methods to validate its remarkable superiority on the aspects of topography reconstruction and speckle removal.

Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

NASA Technical Reports Server (NTRS)

Lee, Charles; Alena, Richard L.; Robinson, Peter

2004-01-01

We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

78 FR 4435 - Notice of Availability of the Restoration Design Energy Project Record of Decision/Approved...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-22

... Project (RDEP) Record of Decision (ROD)/approved Resource Management Plan (RMP) amendments for BLM... DEPARTMENT OF THE INTERIOR Bureau of Land Management [LLAZ910000.L13400000.DT0000.LXSS058A0000] Notice of Availability of the Restoration Design Energy Project Record of Decision/Approved Resource...

A new approach to enhance the performance of decision tree for classifying gene expression data.

PubMed

Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Safety validation of decision trees for hepatocellular carcinoma.

PubMed

Wang, Xian-Qiang; Liu, Zhe; Lv, Wen-Ping; Luo, Ying; Yang, Guang-Yun; Li, Chong-Hui; Meng, Xiang-Fei; Liu, Yang; Xu, Ke-Sen; Dong, Jia-Hong

2015-08-21

To evaluate a different decision tree for safe liver resection and verify its efficiency. A total of 2457 patients underwent hepatic resection between January 2004 and December 2010 at the Chinese PLA General Hospital, and 634 hepatocellular carcinoma (HCC) patients were eligible for the final analyses. Post-hepatectomy liver failure (PHLF) was identified by the association of prothrombin time < 50% and serum bilirubin > 50 μmol/L (the "50-50" criteria), which were assessed at day 5 postoperatively or later. The Swiss-Clavien decision tree, Tokyo University-Makuuchi decision tree, and Chinese consensus decision tree were adopted to divide patients into two groups based on those decision trees in sequence, and the PHLF rates were recorded. The overall mortality and PHLF rate were 0.16% and 3.0%. A total of 19 patients experienced PHLF. The numbers of patients to whom the Swiss-Clavien, Tokyo University-Makuuchi, and Chinese consensus decision trees were applied were 581, 573, and 622, and the PHLF rates were 2.75%, 2.62%, and 2.73%, respectively. Significantly more cases satisfied the Chinese consensus decision tree than the Swiss-Clavien decision tree and Tokyo University-Makuuchi decision tree (P < 0.01，P < 0.01); nevertheless, the latter two shared no difference (P = 0.147). The PHLF rate exhibited no significant difference with respect to the three decision trees. The Chinese consensus decision tree expands the indications for hepatic resection for HCC patients and does not increase the PHLF rate compared to the Swiss-Clavien and Tokyo University-Makuuchi decision trees. It would be a safe and effective algorithm for hepatectomy in patients with hepatocellular carcinoma.

The Effect of Information Level on Human-Agent Interaction for Route Planning

DTIC Science & Technology

2015-12-01

13 Fig. 4 Experiment 1 shows regression results for time spent at DP predicting posttest trust group membership for the high LOI...decision time by pretest trust group membership. Bars denote standard error (SE). DT at DP was evaluated to see if it predicted posttest trust... group . Linear regression indicated that DT at DP was not a significant predictor of posttest trust for the Low or the Medium LOI conditions; however, it

Parenting approaches and digital technology use of preschool age children in a Chinese community.

PubMed

Wu, Cynthia Sau Ting; Fowler, Cathrine; Lam, Winsome Yuk Yin; Wong, Ho Ting; Wong, Charmaine Hei Man; Yuen Loke, Alice

2014-05-07

Young children are using digital technology (DT) devices anytime and anywhere, especially with the invention of smart phones and the replacement of desktop computers with digital tablets. Although research has shown that parents play an important role in fostering and supporting preschoolers' developing maturity and decisions about DT use, and in protecting them from potential risk due to excessive DT exposure, there have been limited studies conducted in Hong Kong focusing on parent-child DT use. This study had three objectives: 1) to explore parental use of DTs with their preschool children; 2) to identify the DT content that associated with child behavioral problems; and 3) to investigate the relationships between approaches adopted by parents to control children's DT use and related preschooler behavioral problems. This exploratory quantitative study was conducted in Hong Kong with 202 parents or guardians of preschool children between the ages of 3 and 6 attending kindergarten. The questionnaire was focused on four aspects, including 1) participants' demographics; 2) pattern of DT use; 3) parenting approach to manage the child's DT use; and 4) child behavioral and health problems related to DT use. Multiple regression analysis was adopted as the main data analysis method for identifying the DT or parental approach-related predictors of the preschooler behavioral problems. In the multiple linear regression model, the 'restrictive approach score' was the only predictor among the three parental approaches (B:1.66, 95% CI: [0.21, 3.11], p < 0.05). Moreover, the viewing of antisocial behavior cartoons by children also significantly increased the tendency of children to have behavioral problem (B:3.84, 95% CI: [1.66, 6.02], p < 0.01). Since preschool children's cognitive and functional abilities are still in the developmental stage, parents play a crucial role in fostering appropriate and safe DT use. It is suggested that parents practice a combination of restrictive, instructive and co-using approaches, rather than a predominately restrictive approach, to facilitate their child's growth and development. Further studies are needed to explore the parent-child relationship and parents' self-efficacy when managing the parent-child DT use, to develop strategies to guide children in healthy DT use.

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

PubMed

Su, Chao-Ton; Wang, Pa-Chun; Chen, Yan-Cheng; Chen, Li-Fei

2012-08-01

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

Decision-Tree Formulation With Order-1 Lateral Execution

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

A compact symbolic formulation enables mapping of an arbitrarily complex decision tree of a certain type into a highly computationally efficient multidimensional software object. The type of decision trees to which this formulation applies is that known in the art as the Boolean class of balanced decision trees. Parallel lateral slices of an object created by means of this formulation can be executed in constant time considerably less time than would otherwise be required. Decision trees of various forms are incorporated into almost all large software systems. A decision tree is a way of hierarchically solving a problem, proceeding through a set of true/false responses to a conclusion. By definition, a decision tree has a tree-like structure, wherein each internal node denotes a test on an attribute, each branch from an internal node represents an outcome of a test, and leaf nodes represent classes or class distributions that, in turn represent possible conclusions. The drawback of decision trees is that execution of them can be computationally expensive (and, hence, time-consuming) because each non-leaf node must be examined to determine whether to progress deeper into a tree structure or to examine an alternative. The present formulation was conceived as an efficient means of representing a decision tree and executing it in as little time as possible. The formulation involves the use of a set of symbolic algorithms to transform a decision tree into a multi-dimensional object, the rank of which equals the number of lateral non-leaf nodes. The tree can then be executed in constant time by means of an order-one table lookup. The sequence of operations performed by the algorithms is summarized as follows: 1. Determination of whether the tree under consideration can be encoded by means of this formulation. 2. Extraction of decision variables. 3. Symbolic optimization of the decision tree to minimize its form. 4. Expansion and transformation of all nested conjunctive-disjunctive paths to a flattened conjunctive form composed only of equality checks when possible. If each reduced conjunctive form contains only equality checks and all of these forms use the same variables, then the decision tree can be reduced to an order-one operation through a table lookup. The speedup to order one is accomplished by distributing each decision variable over a surface of a multidimensional object by mapping the equality constant to an index

The Effect of Information Level on Human-Agent Interaction for Route Planning

DTIC Science & Technology

2015-12-01

χ2 (4, 60) = 11.41, p = 0.022, and Cramer’s V = 0.308, indicating there was no effect of experiment on posttest trust. Pretest trust was not a...decision time by pretest trust group membership. Bars denote standard error (SE). DT at DP was evaluated to see if it predicted posttest trust...0.007, Cramer’s V = 0.344, indicating there was no effect of experiment on posttest trust. Pretest trust was not a significant prediction of total DT

Decision tools in health care: focus on the problem, not the solution.

PubMed

Liu, Joseph; Wyatt, Jeremy C; Altman, Douglas G

2006-01-20

Systematic reviews or randomised-controlled trials usually help to establish the effectiveness of drugs and other health technologies, but are rarely sufficient by themselves to ensure actual clinical use of the technology. The process from innovation to routine clinical use is complex. Numerous computerised decision support systems (DSS) have been developed, but many fail to be taken up into actual use. Some developers construct technologically advanced systems with little relevance to the real world. Others did not determine whether a clinical need exists. With NHS investing 5 billion pounds sterling in computer systems, also occurring in other countries, there is an urgent need to shift from a technology-driven approach to one that identifies and employs the most cost-effective method to manage knowledge, regardless of the technology. The generic term, 'decision tool' (DT), is therefore suggested to demonstrate that these aids, which seem different technically, are conceptually the same from a clinical viewpoint. Many computerised DSSs failed for various reasons, for example, they were not based on best available knowledge; there was insufficient emphasis on their need for high quality clinical data; their development was technology-led; or evaluation methods were misapplied. We argue that DSSs and other computer-based, paper-based and even mechanical decision aids are members of a wider family of decision tools. A DT is an active knowledge resource that uses patient data to generate case specific advice, which supports decision making about individual patients by health professionals, the patients themselves or others concerned about them. The identification of DTs as a consistent and important category of health technology should encourage the sharing of lessons between DT developers and users and reduce the frequency of decision tool projects focusing only on technology. The focus of evaluation should become more clinical, with the impact of computer-based DTs being evaluated against other computer, paper- or mechanical tools, to identify the most cost effective tool for each clinical problem. We suggested the generic term 'decision tool' to demonstrate that decision-making aids, such as computerised DSSs, paper algorithms, and reminders are conceptually the same, so the methods to evaluate them should be the same.

Predictors of Gleason Score (GS) upgrading on subsequent prostatectomy: a single Institution study in a cohort of patients with GS 6

PubMed Central

Mehta, Vikas; Rycyna, Kevin; Baesens, Bart MM; Barkan, Güliz A; Paner, Gladell P; Flanigan, Robert C; Wojcik, Eva M; Venkataraman, Girish

2012-01-01

Background Biopsy Gleason score (bGS) remains an important prognostic indicator for adverse outcomes in Prostate Cancer (PCA). In the light of recent studies purporting difference in prognostic outcomes for the subgroups of GS7 group (primary Gleason pattern 4 vs. 3), upgrading of a bGS of 6 to a GS≥7 has serious implications. We sought to identify pre-operative factors associated with upgrading in a cohort of GS6 patients who underwent prostatectomy. Design We identified 281 cases of GS6 PCA on biopsy with subsequent prostatectomies. Using data on pre-operative variables (age, PSA, biopsy pathology parameters), logistic regression models (LRM) were developed to identify factors that could be used to predict upgrading to GS≥7 on subsequent prostatectomy. A decision tree (DT) was constructed. Results 92 of 281 cases (32.7%) were upgraded on subsequent prostatectomy. LRM identified a model with two variables with statistically significant ability to predict upgrading, including pre-biopsy PSA (Odds Ratio 8.66; 2.03-37.49, 95% CI) and highest percentage of cancer at any single biopsy site (Odds Ratio 1.03, 1.01-1.05, 95% CI). This two-parameter model yielded an area under curve of 0.67. The decision tree was constructed using only 3 leave nodes; with a test set classification accuracy of 70%. Conclusions A simplistic model using clinical and biopsy data is able to predict the likelihood of upgrading of GS with an acceptable level of certainty. External validation of these findings along with development of a nomogram will aid in better stratifying the cohort of low risk patients as based on the GS. PMID:22949931

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

PubMed

El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

2017-02-07

Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B.

PubMed

Chen, Xiao Yu; Ma, Li Zhuang; Chu, Na; Zhou, Min; Hu, Yiyang

2013-01-01

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

PubMed

Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

2018-01-01

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

Application of the intelligent techniques in transplantation databases: a review of articles published in 2009 and 2010.

PubMed

Sousa, F S; Hummel, A D; Maciel, R F; Cohrs, F M; Falcão, A E J; Teixeira, F; Baptista, R; Mancini, F; da Costa, T M; Alves, D; Pisa, I T

2011-05-01

The replacement of defective organs with healthy ones is an old problem, but only a few years ago was this issue put into practice. Improvements in the whole transplantation process have been increasingly important in clinical practice. In this context are clinical decision support systems (CDSSs), which have reflected a significant amount of work to use mathematical and intelligent techniques. The aim of this article was to present consideration of intelligent techniques used in recent years (2009 and 2010) to analyze organ transplant databases. To this end, we performed a search of the PubMed and Institute for Scientific Information (ISI) Web of Knowledge databases to find articles published in 2009 and 2010 about intelligent techniques applied to transplantation databases. Among 69 retrieved articles, we chose according to inclusion and exclusion criteria. The main techniques were: Artificial Neural Networks (ANN), Logistic Regression (LR), Decision Trees (DT), Markov Models (MM), and Bayesian Networks (BN). Most articles used ANN. Some publications described comparisons between techniques or the use of various techniques together. The use of intelligent techniques to extract knowledge from databases of healthcare is increasingly common. Although authors preferred to use ANN, statistical techniques were equally effective for this enterprise. Copyright © 2011 Elsevier Inc. All rights reserved.

Parenting approaches and digital technology use of preschool age children in a Chinese community

PubMed Central

2014-01-01

Background Young children are using digital technology (DT) devices anytime and anywhere, especially with the invention of smart phones and the replacement of desktop computers with digital tablets. Although research has shown that parents play an important role in fostering and supporting preschoolers’ developing maturity and decisions about DT use, and in protecting them from potential risk due to excessive DT exposure, there have been limited studies conducted in Hong Kong focusing on parent-child DT use. This study had three objectives: 1) to explore parental use of DTs with their preschool children; 2) to identify the DT content that associated with child behavioral problems; and 3) to investigate the relationships between approaches adopted by parents to control children’s DT use and related preschooler behavioral problems. Methods This exploratory quantitative study was conducted in Hong Kong with 202 parents or guardians of preschool children between the ages of 3 and 6 attending kindergarten. The questionnaire was focused on four aspects, including 1) participants’ demographics; 2) pattern of DT use; 3) parenting approach to manage the child’s DT use; and 4) child behavioral and health problems related to DT use. Multiple regression analysis was adopted as the main data analysis method for identifying the DT or parental approach-related predictors of the preschooler behavioral problems. Results In the multiple linear regression model, the ‘restrictive approach score’ was the only predictor among the three parental approaches (B:1.66, 95% CI: [0.21, 3.11], p < 0.05). Moreover, the viewing of antisocial behavior cartoons by children also significantly increased the tendency of children to have behavioral problem (B:3.84, 95% CI: [1.66, 6.02], p < 0.01). Conclusions Since preschool children’s cognitive and functional abilities are still in the developmental stage, parents play a crucial role in fostering appropriate and safe DT use. It is suggested that parents practice a combination of restrictive, instructive and co-using approaches, rather than a predominately restrictive approach, to facilitate their child’s growth and development. Further studies are needed to explore the parent-child relationship and parents’ self-efficacy when managing the parent-child DT use, to develop strategies to guide children in healthy DT use. PMID:24887105

Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning.

PubMed

Yu, Huan; Caldwell, Curtis; Mah, Katherine; Mozeg, Daniel

2009-03-01

Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered 18F-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (AZ) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The AZ of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET Coarseness, PET Contrast, and CT Coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC.

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

PubMed

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Image Classification of Human Carcinoma Cells Using Complex Wavelet-Based Covariance Descriptors

PubMed Central

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A. Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-WT) coefficients and several morphological attributes are computed. Directionally selective DT-WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html. PMID:23341908

VC-dimension of univariate decision trees.

PubMed

Yildiz, Olcay Taner

2015-02-01

In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.

The Decision Tree: A Tool for Achieving Behavioral Change.

ERIC Educational Resources Information Center

Saren, Dru

1999-01-01

Presents a "Decision Tree" process for structuring team decision making and problem solving about specific student behavioral goals. The Decision Tree involves a sequence of questions/decisions that can be answered in "yes/no" terms. Questions address reasonableness of the goal, time factors, importance of the goal, responsibilities, safety,…

Development and acceptability testing of decision trees for self-management of prosthetic socket fit in adults with lower limb amputation.

PubMed

Lee, Daniel Joseph; Veneri, Diana A

2018-05-01

The most common complaint lower limb prosthesis users report is inadequacy of a proper socket fit. Adjustments to the residual limb-socket interface can be made by the prosthesis user without consultation of a clinician in many scenarios through skilled self-management. Decision trees guide prosthesis wearers through the self-management process, empowering them to rectify fit issues, or referring them to a clinician when necessary. This study examines the development and acceptability testing of patient-centered decision trees for lower limb prosthesis users. Decision trees underwent a four-stage process: literature review and expert consultation, designing, two-rounds of expert panel review and revisions, and target audience testing. Fifteen lower limb prosthesis users (average age 61 years) reviewed the decision trees and completed an acceptability questionnaire. Participants reported agreement of 80% or above in five of the eight questions related to acceptability of the decision trees. Disagreement was related to the level of experience of the respondent. Decision trees were found to be easy to use, illustrate correct solutions to common issues, and have terminology consistent with that of a new prosthesis user. Some users with greater than 1.5 years of experience would not use the decision trees based on their own self-management skills. Implications for Rehabilitation Discomfort of the residual limb-prosthetic socket interface is the most common reason for clinician visits. Prosthesis users can use decision trees to guide them through the process of obtaining a proper socket fit independently. Newer users may benefit from using the decision trees more than experienced users.

Minimizing the cost of translocation failure with decision-tree models that predict species' behavioral response in translocation sites.

PubMed

Ebrahimi, Mehregan; Ebrahimie, Esmaeil; Bull, C Michael

2015-08-01

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision-tree models for species' translocation, we used data on the short-term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision-tree algorithms (decision tree, decision-tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. © 2015 Society for Conservation Biology.

The option value of delay in health technology assessment.

PubMed

Eckermann, Simon; Willan, Andrew R

2008-01-01

Processes of health technology assessment (HTA) inform decisions under uncertainty about whether to invest in new technologies based on evidence of incremental effects, incremental cost, and incremental net benefit monetary (INMB). An option value to delaying such decisions to wait for further evidence is suggested in the usual case of interest, in which the prior distribution of INMB is positive but uncertain. of estimating the option value of delaying decisions to invest have previously been developed when investments are irreversible with an uncertain payoff over time and information is assumed fixed. However, in HTA decision uncertainty relates to information (evidence) on the distribution of INMB. This article demonstrates that the option value of delaying decisions to allow collection of further evidence can be estimated as the expected value of sample of information (EVSI). For irreversible decisions, delay and trial (DT) is demonstrated to be preferred to adopt and no trial (AN) when the EVSI exceeds expected costs of information, including expected opportunity costs of not treating patients with the new therapy. For reversible decisions, adopt and trial (AT) becomes a potentially optimal strategy, but costs of reversal are shown to reduce the EVSI of this strategy due to both a lower probability of reversal being optimal and lower payoffs when reversal is optimal. Hence, decision makers are generally shown to face joint research and reimbursement decisions (AN, DT and AT), with the optimal choice dependent on costs of reversal as well as opportunity costs of delay and the distribution of prior INMB.

Soft context clustering for F0 modeling in HMM-based speech synthesis

NASA Astrophysics Data System (ADS)

Khorram, Soheil; Sameti, Hossein; King, Simon

2015-12-01

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional `hard' decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this `divide-and-conquer' approach leads to data sparsity, with the consequence that it suffers from poor generalization, meaning that it is unable to accurately predict parameters for models of unseen contexts: the hard decision tree is a weak function approximator. To alleviate this, we propose the soft decision tree, which is a binary decision tree with soft decisions at the internal nodes. In this soft clustering method, internal nodes select both their children with certain membership degrees; therefore, each node can be viewed as a fuzzy set with a context-dependent membership function. The soft decision tree improves model generalization and provides a superior function approximator because it is able to assign each context to several overlapped leaves. In order to use such a soft decision tree to predict the parameters of the HMM output probability distribution, we derive the smoothest (maximum entropy) distribution which captures all partial first-order moments and a global second-order moment of the training samples. Employing such a soft decision tree architecture with maximum entropy distributions, a novel speech synthesis system is trained using maximum likelihood (ML) parameter re-estimation and synthesis is achieved via maximum output probability parameter generation. In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed. Both subjective and objective evaluations were conducted and indicate a considerable improvement over the conventional method.

Decision trees in epidemiological research.

PubMed

Venkatasubramaniam, Ashwini; Wolfson, Julian; Mitchell, Nathan; Barnes, Timothy; JaKa, Meghan; French, Simone

2017-01-01

In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

Pixel-based skin segmentation in psoriasis images.

PubMed

George, Y; Aldeen, M; Garnavi, R

2016-08-01

In this paper, we present a detailed comparison study of skin segmentation methods for psoriasis images. Different techniques are modified and then applied to a set of psoriasis images acquired from the Royal Melbourne Hospital, Melbourne, Australia, with aim of finding the best technique suited for application to psoriasis images. We investigate the effect of different colour transformations on skin detection performance. In this respect, explicit skin thresholding is evaluated with three different decision boundaries (CbCr, HS and rgHSV). Histogram-based Bayesian classifier is applied to extract skin probability maps (SPMs) for different colour channels. This is then followed by using different approaches to find a binary skin map (SM) image from the SPMs. The approaches used include binary decision tree (DT) and Otsu's thresholding. Finally, a set of morphological operations are implemented to refine the resulted SM image. The paper provides detailed analysis and comparison of the performance of the Bayesian classifier in five different colour spaces (YCbCr, HSV, RGB, XYZ and CIELab). The results show that histogram-based Bayesian classifier is more effective than explicit thresholding, when applied to psoriasis images. It is also found that decision boundary CbCr outperforms HS and rgHSV. Another finding is that the SPMs of Cb, Cr, H and B-CIELab colour bands yield the best SMs for psoriasis images. In this study, we used a set of 100 psoriasis images for training and testing the presented methods. True Positive (TP) and True Negative (TN) are used as statistical evaluation measures.

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

Creating ensembles of decision trees through sampling

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2005-08-30

A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

Bioinformatics in proteomics: application, terminology, and pitfalls.

PubMed

Wiemer, Jan C; Prokudin, Alexander

2004-01-01

Bioinformatics applies data mining, i.e., modern computer-based statistics, to biomedical data. It leverages on machine learning approaches, such as artificial neural networks, decision trees and clustering algorithms, and is ideally suited for handling huge data amounts. In this article, we review the analysis of mass spectrometry data in proteomics, starting with common pre-processing steps and using single decision trees and decision tree ensembles for classification. Special emphasis is put on the pitfall of overfitting, i.e., of generating too complex single decision trees. Finally, we discuss the pros and cons of the two different decision tree usages.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

76 FR 56791 - Notice of Availability of Record of Decision for the Tropic To Hatch (Garkane) 138 kV...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-14

... DEPARTMENT OF THE INTERIOR Bureau of Land Management [LLUT0300000 L17110000 DT0000 24 1A] Notice of Availability of Record of Decision for the Tropic To Hatch (Garkane) 138 kV Transmission Line... Hatch (Garkane) 138 kilovolt (kV) Transmission Line Environmental Impact Statement (EIS) and the...

Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

PubMed

Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

2015-11-01

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.

Multi-test decision tree and its application to microarray data classification.

PubMed

Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

2014-05-01

The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Using histograms to introduce randomization in the generation of ensembles of decision trees

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

2005-02-22

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

Automated diagnosis of epilepsy using CWT, HOS and texture parameters.

PubMed

Acharya, U Rajendra; Yanti, Ratna; Zheng, Jia Wei; Krishnan, M Muthu Rama; Tan, Jen Hong; Martis, Roshan Joy; Lim, Choo Min

2013-06-01

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Signatures of soft sweeps across the Dt1 locus underlying determinate growth habit in soya bean [Glycine max (L.) Merr.].

PubMed

Zhong, Limei; Yang, Qiaomei; Yan, Xin; Yu, Chao; Su, Liu; Zhang, Xifeng; Zhu, Youlin

2017-09-01

Determinate growth habit is an agronomically important trait associated with domestication in soya bean. Previous studies have demonstrated that the emergence of determinacy is correlated with artificial selection on four nonsynonymous mutations in the Dt1 gene. To better understand the signatures of the soft sweeps across the Dt1 locus and track the origins of the determinate alleles, we examined patterns of nucleotide variation in Dt1 and the surrounding genomic region of approximately 800 kb. Four local, asymmetrical hard sweeps on four determinate alleles, sized approximately 660, 120, 220 and 150 kb, were identified, which constitute the soft sweeps for the adaptation. These variable-sized sweeps substantially reflected the strength and timing of selection and indicated that the selection on the alleles had been completed rapidly within half a century. Statistics of EHH, iHS, H12 and H2/H1 based on haplotype data had the power to detect the soft sweeps, revealing distinct signatures of extensive long-range LD and haplotype homozygosity, and multiple frequent adaptive haplotypes. A haplotype network constructed for Dt1 and a phylogenetic tree based on its extended haplotype block implied independent sources of the adaptive alleles through de novo mutations or rare standing variation in quick succession during the selective phase, strongly supporting multiple origins of the determinacy. We propose that the adaptation of soya bean determinacy is guided by a model of soft sweeps and that this model might be indispensable during crop domestication or evolution. © 2017 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

2009-01-01

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Objective consensus from decision trees.

PubMed

Putora, Paul Martin; Panje, Cedric M; Papachristofilou, Alexandros; Dal Pra, Alan; Hundsberger, Thomas; Plasswilm, Ludwig

2014-12-05

Consensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources. Based on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus. Based on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters. Recommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

Introduction to D-He(3) fusion reactors

NASA Technical Reports Server (NTRS)

Vlases, G. C.; Steinhauer, L. C.

1989-01-01

A review and evaluation of D-He(3) fusion reactor technology is presented. The advantages and disadvantages of the D-He(3) and D-T reactor cycles are outlined and compared. In addition, the general design features of D-He(3) tokamaks and field reversed configuration (FRC) reactors are described and the relative merits of each are compared. It is concluded that both tokamaks and FRC's offer certain advantages, and that the ultimate decision as to which to persue for terrestrial power generation will depend heavily on how the physics performance of each of them develops over the next few years. It is clear that the D-He(3) fuel cycle offers marked advantages over the D-T cycle. Although the physics requirements for D-He(3) are more demanding, the overwhelming advantages resulting from the two order of magnitude reduction of neutron flux are expected to lead to a shorter time to commercialization than for the D-T cycle.

Introduction to D-He(3) fusion reactors

NASA Astrophysics Data System (ADS)

Vlases, G. C.; Steinhauer, L. C.

1989-07-01

A review and evaluation of D-He(3) fusion reactor technology is presented. The advantages and disadvantages of the D-He(3) and D-T reactor cycles are outlined and compared. In addition, the general design features of D-He(3) tokamaks and field reversed configuration (FRC) reactors are described and the relative merits of each are compared. It is concluded that both tokamaks and FRC's offer certain advantages, and that the ultimate decision as to which to persue for terrestrial power generation will depend heavily on how the physics performance of each of them develops over the next few years. It is clear that the D-He(3) fuel cycle offers marked advantages over the D-T cycle. Although the physics requirements for D-He(3) are more demanding, the overwhelming advantages resulting from the two order of magnitude reduction of neutron flux are expected to lead to a shorter time to commercialization than for the D-T cycle.

Potential Information and Decision Support System Applications for a Civil Engineering RED HORSE Squadron.

DTIC Science & Technology

1987-09-01

APPLICATIONS FOR A CIVIL ENGINEERILaG RED HORSE SQUADRON THESIS Arvil E. White III Captain, USAF AFIT/GE:4/LSM/87S-27 .... DEPARTMENT OF THE AIR FORCE...DT1TO-SJAN 0 419880 POTENTIAL INFORMATION AND DECISION SUPPORT SYSTEM APPLICATIONS FOR A CIVIL ENGINEERILiG RED HORSE SQUADRON IAooession For THESIS NI R...INFORMATION AND DECISION SUPPORT SYSTrEM APPLICATIONS FOR A CIVIL ENGINEERINGX :.. 4. RED HORSE SQUADRON - THESIS -4 Presented to the Faculty of the

The decision tree approach to classification

NASA Technical Reports Server (NTRS)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Icing detection from geostationary satellite data using machine learning approaches

NASA Astrophysics Data System (ADS)

Lee, J.; Ha, S.; Sim, S.; Im, J.

2015-12-01

Icing can cause a significant structural damage to aircraft during flight, resulting in various aviation accidents. Icing studies have been typically performed using two approaches: one is a numerical model-based approach and the other is a remote sensing-based approach. The model based approach diagnoses aircraft icing using numerical atmospheric parameters such as temperature, relative humidity, and vertical thermodynamic structure. This approach tends to over-estimate icing according to the literature. The remote sensing-based approach typically uses meteorological satellite/ground sensor data such as Geostationary Operational Environmental Satellite (GOES) and Dual-Polarization radar data. This approach detects icing areas by applying thresholds to parameters such as liquid water path and cloud optical thickness derived from remote sensing data. In this study, we propose an aircraft icing detection approach which optimizes thresholds for L1B bands and/or Cloud Optical Thickness (COT) from Communication, Ocean and Meteorological Satellite-Meteorological Imager (COMS MI) and newly launched Himawari-8 Advanced Himawari Imager (AHI) over East Asia. The proposed approach uses machine learning algorithms including decision trees (DT) and random forest (RF) for optimizing thresholds of L1B data and/or COT. Pilot Reports (PIREPs) from South Korea and Japan were used as icing reference data. Results show that RF produced a lower false alarm rate (1.5%) and a higher overall accuracy (98.8%) than DT (8.5% and 75.3%), respectively. The RF-based approach was also compared with the existing COMS MI and GOES-R icing mask algorithms. The agreements of the proposed approach with the existing two algorithms were 89.2% and 45.5%, respectively. The lower agreement with the GOES-R algorithm was possibly due to the high uncertainty of the cloud phase product from COMS MI.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

PubMed

Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

2013-09-01

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

PubMed

Che, Dongsheng; Liu, Qi; Rasheed, Khaled; Tao, Xiuping

2011-01-01

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

ERIC Educational Resources Information Center

Poe, Retta E.

1988-01-01

Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

High early event rates in patients with questionable eligibility for advanced heart failure therapies: Results from the Medical Arm of Mechanically Assisted Circulatory Support (Medamacs) Registry.

PubMed

Ambardekar, Amrut V; Forde-McLean, Rhondalyn C; Kittleson, Michelle M; Stewart, Garrick C; Palardy, Maryse; Thibodeau, Jennifer T; DeVore, Adam D; Mountis, Maria M; Cadaret, Linda; Teuteberg, Jeffrey J; Pamboukian, Salpy V; Cantor, Ryan S; Lindenfeld, JoAnn

2016-06-01

The prognosis of ambulatory patients with advanced heart failure (HF) who are not yet inotrope dependent and implications for evaluation and timing for transplant or destination therapy with a left ventricular assist device (DT-LVAD) are unknown. We hypothesized that the characteristics defining eligibility for advanced HF therapies would be a primary determinant of outcomes in these patients. Ambulatory patients with advanced HF (New York Heart Association class III-IV, Interagency Registry for Mechanically Assisted Circulatory Support profiles 4-7) were enrolled across 11 centers from May 2013 to February 2015. Patients were stratified into 3 groups: likely transplant eligible, DT-LVAD eligible, and ineligible for both transplant and DT-LVAD. Clinical characteristics were collected, and patients were prospectively followed for death, transplant, and left ventricular assist device implantation. The study enrolled 144 patients with a mean follow-up of 10 ± 6 months. Patients in the ineligible cohort (n = 43) had worse congestion, renal function, and anemia compared with transplant (n = 51) and DT-LVAD (n = 50) eligible patients. Ineligible patients had higher mortality (23.3% vs 8.0% in DT-LVAD group and 5.9% in transplant group, p = 0.02). The differences in mortality were related to lower rates of transplantation (11.8% in transplant group vs 2.0% in DT-LVAD group and 0% in ineligible group, p = 0.02) and left ventricular assist device implantation (15.7% in transplant group vs 2.0% in DT-LVAD group and 0% in ineligible group, p < 0.01). Ambulatory patients with advanced HF who were deemed ineligible for transplant and DT-LVAD had markers of greater HF severity and a higher rate of mortality compared with patients eligible for transplant or DT-LVAD. The high early event rate in this group emphasizes the need for timely evaluation and decision making regarding lifesaving therapies. Copyright © 2016 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

The value of decision tree analysis in planning anaesthetic care in obstetrics.

PubMed

Bamber, J H; Evans, S A

2016-08-01

The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Building of fuzzy decision trees using ID3 algorithm

NASA Astrophysics Data System (ADS)

Begenova, S. B.; Avdeenko, T. V.

2018-05-01

Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

Evolutionary Algorithm Based Automated Reverse Engineering and Defect Discovery

DTIC Science & Technology

2007-09-21

a previous application of a GP as a data mining function to evolve fuzzy decision trees symbolically [3-5], the terminal set consisted of fuzzy...of input and output information is required. In the case of fuzzy decision trees, the database represented a collection of scenarios about which the...fuzzy decision tree to be evolved would make decisions . The database also had entries created by experts representing decisions about the scenarios

Phase synchronization based on a Dual-Tree Complex Wavelet Transform

NASA Astrophysics Data System (ADS)

Ferreira, Maria Teodora; Domingues, Margarete Oliveira; Macau, Elbert E. N.

2016-11-01

In this work, we show the applicability of our Discrete Complex Wavelet Approach (DCWA) to verify the phenomenon of phase synchronization transition in two coupled chaotic Lorenz systems. DCWA is based on the phase assignment from complex wavelet coefficients obtained by using a Dual-Tree Complex Wavelet Transform (DT-CWT). We analyzed two coupled chaotic Lorenz systems, aiming to detect the transition from non-phase synchronization to phase synchronization. In addition, we check how good is the method in detecting periods of 2π phase-slips. In all experiments, DCWA is compared with classical phase detection methods such as the ones based on arctangent and Hilbert transform showing a much better performance.

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

DOEpatents

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Automated rule-base creation via CLIPS-Induce

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.

1994-01-01

Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

Decision tree methods: applications for classification and prediction.

PubMed

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Learning from examples - Generation and evaluation of decision trees for software resource analysis

NASA Technical Reports Server (NTRS)

Selby, Richard W.; Porter, Adam A.

1988-01-01

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

ERIC Educational Resources Information Center

Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

2009-01-01

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

PubMed Central

Masías, Víctor H.; Krause, Mariane; Valdés, Nelson; Pérez, J. C.; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice. PMID:25914657

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process.

PubMed

Masías, Víctor H; Krause, Mariane; Valdés, Nelson; Pérez, J C; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior.

PubMed

Delgado-Gomez, D; Baca-Garcia, E; Aguado, D; Courtet, P; Lopez-Castroman, J

2016-12-01

Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. Copyright © 2016 Elsevier B.V. All rights reserved.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

PubMed

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

NASA Astrophysics Data System (ADS)

Wahyuni, Sri

2018-03-01

Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

An Improved Decision Tree for Predicting a Major Product in Competing Reactions

ERIC Educational Resources Information Center

Graham, Kate J.

2014-01-01

When organic chemistry students encounter competing reactions, they are often overwhelmed by the task of evaluating multiple factors that affect the outcome of a reaction. The use of a decision tree is a useful tool to teach students to evaluate a complex situation and propose a likely outcome. Specifically, a decision tree can help students…

Decision Tree Phytoremediation

DTIC Science & Technology

1999-12-01

aromatic hydrocarbons, and landfill leachates . Phytoremediation has been used for point and nonpoint source hazardous waste control. 1.2 Types of... Phytoremediation Prepared by Interstate Technology and Regulatory Cooperation Work Group Phytoremediation Work Team December 1999 Decision Tree...1999 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Phytoremediation Decision Tree 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

PubMed

Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

2012-11-21

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

PubMed Central

2012-01-01

Background This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor. PMID:23171000

A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

PubMed

Nair, Shalini Rajandran; Tan, Li Kuo; Mohd Ramli, Norlisah; Lim, Shen Yang; Rahmat, Kartini; Mohd Nor, Hazman

2013-06-01

To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

NASA Astrophysics Data System (ADS)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

Multivariate analysis of flow cytometric data using decision trees.

PubMed

Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

2012-01-01

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

SU-E-I-30: Image Analysis in Ultrasonography for Diagnosis of Sjoegren's Syndrome Using Dual-Tree Complex Wavelet Transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matsui, T; Ohki, M; Nakamura, T

Purpose: Sjoegren's syndrome (SS) is an autoimmune disease invading mainly salivary and lacrimal glands. Ultrasonography is used for an initial and non-invasive examination of this disease. However, the ultrasonography diagnosis tends to lack in objectivity and depends on the operator's skills. The purpose of this study is to propose a computer-aided diagnosis (CAD) system for SS based on a dual-tree complex wavelet transform (DT-CWT) and machine learning. Methods: The subjects of this study were 174 patients suspected of having SS at Nagasaki University Hospital and examined with ultrasonography of the parotid glands. Out of these patients, 77 patients were diagnosedmore » with SS by sialography. A region of interest (ROI) of 128 × 128 pixels was set within the parotid gland that was indicated by a dental radiologist. The DT-CWT was applied to the images in the ROI and every image was decomposed into 72 sub-images of the real and imaginary components in six different resolution levels and six orientations. The statistical features of the sub-image were calculated and used as data input for the support vector machine (SVM) classifier for the detection of SS. A ten-fold cross-validation was employed to verify the Resultof SVM. The accuracy of diagnosis was compared by a CAD system with a human observer performance. Results: The sensitivity, specificity, and accuracy in the detection of SS were 95%, 86%, and 91% through our CAD system respectively, while those by a human observer were 84%, 81%, and 83% respectively. Conclusion: The proposed computer-aided diagnosis system for Sjoegren's syndrome in ultrasonography based on dual-tree complex wavelet transform had a better performance than a human observer.« less

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 2 2013-01-01 2013-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 2 2014-01-01 2014-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 2 2012-01-01 2012-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 2 2011-01-01 2011-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

Improved Frame Mode Selection for AMR-WB+ Based on Decision Tree

NASA Astrophysics Data System (ADS)

Kim, Jong Kyu; Kim, Nam Soo

In this letter, we propose a coding mode selection method for the AMR-WB+ audio coder based on a decision tree. In order to reduce computation while maintaining good performance, decision tree classifier is adopted with the closed loop mode selection results as the target classification labels. The size of the decision tree is controlled by pruning, so the proposed method does not increase the memory requirement significantly. Through an evaluation test on a database covering both speech and music materials, the proposed method is found to achieve a much better mode selection accuracy compared with the open loop mode selection module in the AMR-WB+.

Activity classification using realistic data from wearable sensors.

PubMed

Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka

2006-01-01

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

A universal hybrid decision tree classifier design for human activity classification.

PubMed

Chien, Chieh; Pottie, Gregory J

2012-01-01

A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. The system was tested using seven subjects each monitored by 14 triaxial accelerometers. Each subject performed fourteen different activities typical of daily life. Using leave-one-out cross validation, our decision tree produced average classification accuracies of 89.9%. In contrast, the MATLAB personalized tree classifiers using Gini's diversity index as the split criterion followed by optimally tuning the thresholds for each subject yielded 69.2%.

Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis.

PubMed

Wang, Ting; Li, Weiying; Zheng, Xiaofeng; Lin, Zhifen; Kong, Deyang

2014-02-01

During the last past decades, there is an increasing number of studies about estrogenic activities of the environmental pollutants on amphibians and many determination methods have been proposed. However, these determination methods are time-consuming and expensive, and a rapid and simple method to screen and test the chemicals for estrogenic activities to amphibians is therefore imperative. Herein is proposed a new decision tree formulated not only with physicochemical parameters but also a biological parameter that was successfully used to screen estrogenic activities of the chemicals on amphibians. The biological parameter, CDOCKER interaction energy (Ebinding ) between chemicals and the target proteins was calculated based on the method of molecular docking, and it was used to revise the decision tree formulated by Hong only with physicochemical parameters for screening estrogenic activity of chemicals in rat. According to the correlation between Ebinding of rat and Xenopus laevis, a new decision tree for estrogenic activities in Xenopus laevis is finally proposed. Then it was validated by using the randomly 8 chemicals which can be frequently exposed to Xenopus laevis, and the agreement between the results from the new decision tree and the ones from experiments is generally satisfactory. Consequently, the new decision tree can be used to screen the estrogenic activities of the chemicals, and combinational use of the Ebinding and classical physicochemical parameters can greatly improves Hong's decision tree. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparing wavefront-optimized, wavefront-guided and topography-guided laser vision correction: clinical outcomes using an objective decision tree.

PubMed

Stonecipher, Karl; Parrish, Joseph; Stonecipher, Megan

2018-05-18

This review is intended to update and educate the reader on the currently available options for laser vision correction, more specifically, laser-assisted in-situ keratomileusis (LASIK). In addition, some related clinical outcomes data from over 1000 cases performed over a 1-year are presented to highlight some differences between the various treatment profiles currently available including the rapidity of visual recovery. The cases in question were performed on the basis of a decision tree to segregate patients on the basis of anatomical, topographic and aberrometry findings; the decision tree was formulated based on the data available in some of the reviewed articles. Numerous recent studies reported in the literature provide data related to the risks and benefits of LASIK; alternatives to a laser refractive procedure are also discussed. The results from these studies have been used to prepare a decision tree to assist the surgeon in choosing the best option for the patient based on the data from several standard preoperative diagnostic tests. The data presented here should aid surgeons in understanding the effects of currently available LASIK treatment profiles. Surgeons should also be able to appreciate how the findings were used to create a decision tree to help choose the most appropriate treatment profile for patients. Finally, the retrospective evaluation of clinical outcomes based on the decision tree should provide surgeons with a realistic expectation for their own outcomes should they adopt such a decision tree in their own practice.

Installation Restoration Program, Phase 1. Records Search, Wheeler Air Force Base, Oahu, Hawaii

DTIC Science & Technology

1983-07-01

the 3vegetation was already exotic, consisting of trees such as guava , koa haole, eucalyptus and silver oak, and shrubs and 3 grasses including lantana...Alkalioo Soap 5 gal GrounOd S~r Of f Base Fire Pit fir.Pit Of f Base PmecI" CoCAClo 20$ P 680 1S Sa.L Cro-..d tAint 10-20 gal 8w L..dt±u Off km. Thinmar

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

PubMed

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P < 0.01). A clinically useful classification tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

DOE PAGES

Liao, Huafei N.; Groth, Katrina; Stevens-Adams, Susan

2015-07-29

Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification aremore » discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.« less

A comparison between skeleton and bounding box models for falling direction recognition

NASA Astrophysics Data System (ADS)

Narupiyakul, Lalita; Srisrisawang, Nitikorn

2017-12-01

Falling is an injury that can lead to a serious medical condition in every range of the age of people. However, in the case of elderly, the risk of serious injury is much higher. Due to the fact that one way of preventing serious injury is to treat the fallen person as soon as possible, several works attempted to implement different algorithms to recognize the fall. Our work compares the performance of two models based on features extraction: (i) Body joint data (Skeleton Data) which are the joint's positions in 3 axes and (ii) Bounding box (Box-size Data) covering all body joints. Machine learning algorithms that were chosen are Decision Tree (DT), Naïve Bayes (NB), K-nearest neighbors (KNN), Linear discriminant analysis (LDA), Voting Classification (VC), and Gradient boosting (GB). The results illustrate that the models trained with Skeleton data are performed far better than those trained with Box-size data (with an average accuracy of 94-81% and 80-75%, respectively). KNN shows the best performance in both Body joint model and Bounding box model. In conclusion, KNN with Body joint model performs the best among the others.

Modeling activity recognition of multi resident using label combination of multi label classification in smart home

NASA Astrophysics Data System (ADS)

Mohamed, Raihani; Perumal, Thinagaran; Sulaiman, Md Nasir; Mustapha, Norwati; Zainudin, M. N. Shah

2017-10-01

Pertaining to the human centric concern and non-obtrusive way, the ambient sensor type technology has been selected, accepted and embedded in the environment in resilient style. Human activities, everyday are gradually becoming complex and thus complicate the inferences of activities when it involving the multi resident in the same smart environment. Current works solutions focus on separate model between the resident, activities and interactions. Some study use data association and extra auxiliary of graphical nodes to model human tracking information in an environment and some produce separate framework to incorporate the auxiliary for interaction feature model. Thus, recognizing the activities and which resident perform the activity at the same time in the smart home are vital for the smart home development and future applications. This paper will cater the above issue by considering the simplification and efficient method using the multi label classification framework. This effort eliminates time consuming and simplifies a lot of pre-processing tasks comparing with previous approach. Applications to the multi resident multi label learning in smart home problems shows the LC (Label Combination) using Decision Tree (DT) as base classifier can tackle the above problems.

Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

PubMed Central

in ’t Veen, Johannes C.C.M.; Dekhuijzen, P.N. Richard; van Heijst, Ellen; Kocks, Janwillem W.H.; Muilwijk-Kroes, Jacqueline B.; Chavannes, Niels H.; van der Molen, Thys

2016-01-01

The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool. PMID:27730177

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

A Decision Tree for Nonmetric Sex Assessment from the Skull.

PubMed

Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

2018-01-01

This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

2015-12-01

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

DTIC Science & Technology

2008-04-01

REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

PubMed

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

Pruning a decision tree for selecting computer-related assistive devices for people with disabilities.

PubMed

Chi, Chia-Fen; Tseng, Li-Kai; Jang, Yuh

2012-07-01

Many disabled individuals lack extensive knowledge about assistive technology, which could help them use computers. In 1997, Denis Anson developed a decision tree of 49 evaluative questions designed to evaluate the functional capabilities of the disabled user and choose an appropriate combination of assistive devices, from a selection of 26, that enable the individual to use a computer. In general, occupational therapists guide the disabled users through this process. They often have to go over repetitive questions in order to find an appropriate device. A disabled user may require an alphanumeric entry device, a pointing device, an output device, a performance enhancement device, or some combination of these. Therefore, the current research eliminates redundant questions and divides Anson's decision tree into multiple independent subtrees to meet the actual demand of computer users with disabilities. The modified decision tree was tested by six disabled users to prove it can determine a complete set of assistive devices with a smaller number of evaluative questions. The means to insert new categories of computer-related assistive devices was included to ensure the decision tree can be expanded and updated. The current decision tree can help the disabled users and assistive technology practitioners to find appropriate computer-related assistive devices that meet with clients' individual needs in an efficient manner.

Uncertain decision tree inductive inference

NASA Astrophysics Data System (ADS)

Zarban, L.; Jafari, S.; Fakhrahmad, S. M.

2011-10-01

Induction is the process of reasoning in which general rules are formulated based on limited observations of recurring phenomenal patterns. Decision tree learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed such as CLS, ID3, Assistant C4.5, REPTree and Random Tree. These algorithms suffer from some major shortcomings. In this article, after discussing the main limitations of the existing methods, we introduce a new decision tree induction algorithm, which overcomes all the problems existing in its counterparts. The new method uses bit strings and maintains important information on them. This use of bit strings and logical operation on them causes high speed during the induction process. Therefore, it has several important features: it deals with inconsistencies in data, avoids overfitting and handles uncertainty. We also illustrate more advantages and the new features of the proposed method. The experimental results show the effectiveness of the method in comparison with other methods existing in the literature.

Comparative Issues and Methods in Organizational Diagnosis. Report II. The Decision Tree Approach.

DTIC Science & Technology

organizational diagnosis . The advantages and disadvantages of the decision-tree approach generally, and in this study specifically, are examined. A pre-test, using a civilian sample of 174 work groups with Survey of Organizations data, was conducted to assess various decision-tree classification criteria, in terms of their similarity to the distance function used by Bowers and Hausser (1977). The results suggested the use of a large developmental sample, which should result in more distinctly defined boundary lines between classification profiles. Also, the decision matrix

Cognitive-motor dual-task ability of athletes with and without intellectual impairment.

PubMed

Van Biesen, Debbie; Jacobs, Lore; McCulloch, Katina; Janssens, Luc; Vanlandewijck, Yves C

2018-03-01

Cognition is important in many sports, for example, making split-second-decisions under pressure, or memorising complex movement sequences. The dual-task (DT) paradigm is an ecologically valid approach for the assessment of cognitive function in conjunction with motor demands. This study aimed to determine the impact of impaired intelligence on DT performance. The motor task required balancing on one leg on a beam, and the cognitive task was a multiple-object-tracking (MOT) task assessing dynamic visual-search capacity. The sample included 206 well-trained athletes with and without intellectual impairment (II), matched for sport, age and training volume (140 males, 66 females, M age = 23.2 ± 4.1 years, M training experience = 12.3 ± 5.7 years). In the single-task condition, II-athletes showed reduced balance control (F = 55.9, P < .001, η 2  = .23) and reduced MOT (F = 86.3, P < .001, η 2  = .32) compared to the control group. A mixed-model ANCOVA revealed significant differences in DT performance for the balance and the MOT task between both groups. The DT costs were significantly larger for the II-athletes (-8.28% versus -1.34% for MOT and -33.13% versus -12.89% for balance). The assessment of MOT in a DT paradigm provided insight in how impaired intelligence constrains the ability of II-athletes to successfully perform at the highest levels in the complex and dynamical sport-environment.

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

PubMed

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

MRI-based decision tree model for diagnosis of biliary atresia.

PubMed

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

PubMed

Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

2015-07-01

The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PubMed

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

PubMed

Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

2017-08-16

The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation.

PubMed

Shao, Q; Rowe, R C; York, P

2007-06-01

Understanding of the cause-effect relationships between formulation ingredients, process conditions and product properties is essential for developing a quality product. However, the formulation knowledge is often hidden in experimental data and not easily interpretable. This study compares neurofuzzy logic and decision tree approaches in discovering hidden knowledge from an immediate release tablet formulation database relating formulation ingredients (silica aerogel, magnesium stearate, microcrystalline cellulose and sodium carboxymethylcellulose) and process variables (dwell time and compression force) to tablet properties (tensile strength, disintegration time, friability, capping and drug dissolution at various time intervals). Both approaches successfully generated useful knowledge in the form of either "if then" rules or decision trees. Although different strategies are employed by the two approaches in generating rules/trees, similar knowledge was discovered in most cases. However, as decision trees are not able to deal with continuous dependent variables, data discretisation procedures are generally required.

Parallel object-oriented decision tree system

DOEpatents

Kamath,; Chandrika, Cantu-Paz [Dublin, CA; Erick, [Oakland, CA

2006-02-28

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

DTIC Science & Technology

1984-10-01

O M coF=F;; UMBER2. GOVT ACCE5SION NO.1 3 . REC,PINS :A7AL:,G NUMBER ( ’eneration and Terminat_,on :)f Binary D-ecision jC j ik; Trees for Nonnararetrc...1-I . v)IAMO 0~I4 EDvt" O F I 00 . 3 15I OR%.OL.ETL - S-S OCTOBER 1984 LIDS-P-1411 GENERATION AND TERMINATION OF BINARY DECISION TREES FOR...minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. 0 0 0/ 6 0¢ A 3 I. Introduction We state

Development of a decision-making tool for reporting drivers with mild dementia and mild cognitive impairment to transportation administrators.

PubMed

Cameron, Duncan H; Zucchero Sarracini, Carla; Rozmovits, Linda; Naglie, Gary; Herrmann, Nathan; Molnar, Frank; Jordan, John; Byszewski, Anna; Tang-Wai, David; Dow, Jamie; Frank, Christopher; Henry, Blair; Pimlott, Nicholas; Seitz, Dallas; Vrkljan, Brenda; Taylor, Rebecca; Masellis, Mario; Rapoport, Mark J

2017-09-01

Driving in persons with dementia poses risks that must be counterbalanced with the importance of the care for autonomy and mobility. Physicians often find substantial challenges in the assessment and reporting of driving safety for persons with dementia. This paper describes a driving in dementia decision tool (DD-DT) developed to aid physicians in deciding when to report older drivers with either mild dementia or mild cognitive impairment to local transportation administrators. A multi-faceted, computerized decision support tool was developed, using a systematic literature and guideline review, expert opinion from an earlier Delphi study, as well as qualitative interviews and focus groups with physicians, caregivers of former drivers with dementia, and transportation administrators. The tool integrates inputs from the physician-user about the patient's clinical and driving history as well as cognitive findings, and it produces a recommendation for reporting to transportation administrators. This recommendation is translated into a customized reporting form for the transportation authority, if applicable, and additional resources are provided for the patient and caregiver. An innovative approach was needed to develop the DD-DT. The literature and guideline review confirmed the algorithm derived from the earlier Delphi study, and barriers identified in the qualitative research were incorporated into the design of the tool.

The Decision Tree for Teaching Management of Uncertainty

ERIC Educational Resources Information Center

Knaggs, Sara J.; And Others

1974-01-01

A 'decision tree' consists of an outline of the patient's symptoms and a logic for decision and action. It is felt that this approach to the decisionmaking process better facilitates each learner's application of his own level of knowledge and skills. (Author)

Predicting metabolic syndrome using decision tree and support vector machine methods.

PubMed

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.

Cost-effectiveness Analysis with Influence Diagrams.

PubMed

Arias, M; Díez, F J

2015-01-01

Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEA for very small problems. To develop a method for CEA in problems involving several dozen variables. We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. The evaluation of an ID returns a set of intervals for the willingness to pay - separated by cost-effectiveness thresholds - and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on several IDs whose equivalent decision trees contain millions of branches. IDs can perform CEA on large problems that cannot be analyzed with decision trees.

Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology.

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Liu, Chen-Chung; Ou, Kuo-Liang; Liu, Baw-Jhiune

2000-01-01

Discusses the use of Web logs to record student behavior that can assist teachers in assessing performance and making curriculum decisions for distance learning students who are using Web-based learning systems. Adopts decision tree and data cube information processing methodologies for developing more effective pedagogical strategies. (LRW)

Assessing School Readiness for a Practice Arrangement Using Decision Tree Methodology.

ERIC Educational Resources Information Center

Barger, Sara E.

1998-01-01

Questions in a decision-tree address mission, faculty interest, administrative support, and practice plan as a way of assessing arrangements for nursing faculty's clinical practice. Decisions should be based on congruence between the human resource allocation and the reward systems. (SK)

Automated Decision Tree Classification of Corneal Shape

PubMed Central

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems. PMID:16357645

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

PubMed

Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

2016-02-01

To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

On Parallelism and the Penman Natural Language Generation System.

DTIC Science & Technology

1988-04-01

TagfiniteA Tagsubject L untag ed Figure 2-2: System network with choosers & realization statements 7 decision . We will give a more detailed account of...2: enter the current system. The chooser of the system is in charge of * selection of features. The chooser is itself a decision tree with certain...organization of a chooser is the same as a decision (discrimination) tree, and each branching point in the tree is defined by Ask operation. For example, in

Jane and Johnny Love Math: Recognizing and Encouraging Mathematical Talent in Elementary Students; A Guidebook for Educators and Parents.

ERIC Educational Resources Information Center

Lupkowski, Ann E.; Assouline, Susan G.

This book is a guide for parents and teachers of mathematically talented elementary school students. Chapters and sections include: (1) "Overview"; (2) "Historical and Current Perspectives"; (3) "Making Informed Educational Decisions"; (4) "Diagnostic Testing Followed by Prescriptive Instruction: SMPY's DT to PI…

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, P.; Beaudet, P.

1980-01-01

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Decision-Tree Analysis for Predicting First-Time Pass/Fail Rates for the NCLEX-RN® in Associate Degree Nursing Students.

PubMed

Chen, Hsiu-Chin; Bennett, Sean

2016-08-01

Little evidence shows the use of decision-tree algorithms in identifying predictors and analyzing their associations with pass rates for the NCLEX-RN(®) in associate degree nursing students. This longitudinal and retrospective cohort study investigated whether a decision-tree algorithm could be used to develop an accurate prediction model for the students' passing or failing the NCLEX-RN. This study used archived data from 453 associate degree nursing students in a selected program. The chi-squared automatic interaction detection analysis of the decision trees module was used to examine the effect of the collected predictors on passing/failing the NCLEX-RN. The actual percentage scores of Assessment Technologies Institute®'s RN Comprehensive Predictor(®) accurately identified students at risk of failing. The classification model correctly classified 92.7% of the students for passing. This study applied the decision-tree model to analyze a sequence database for developing a prediction model for early remediation in preparation for the NCLEXRN. [J Nurs Educ. 2016;55(8):454-457.]. Copyright 2016, SLACK Incorporated.

Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer.

PubMed

Suner, Aslı; Çelikoğlu, Can Cengiz; Dicle, Oğuz; Sökmen, Selman

2012-09-01

The aim of the study is to determine the most appropriate method for construction of a sequential decision tree in the management of rectal cancer, using various patient-specific criteria and treatments such as surgery, chemotherapy, and radiotherapy. An analytic hierarchy process (AHP) was used to determine the priorities of variables. Relevant criteria used in two decision steps and their relative priorities were established by a panel of five general surgeons. Data were collected via a web-based application and analyzed using the "Expert Choice" software specifically developed for the AHP. Consistency ratios in the AHP method were calculated for each set of judgments, and the priorities of sub-criteria were determined. A sequential decision tree was constructed for the best treatment decision process, using priorities determined by the AHP method. Consistency ratios in the AHP method were calculated for each decision step, and the judgments were considered consistent. The tumor-related criterion "presence of perforation" (0.331) and the patient-surgeon-related criterion "surgeon's experience" (0.630) had the highest priority in the first decision step. In the second decision step, the tumor-related criterion "the stage of the disease" (0.230) and the patient-surgeon-related criterion "surgeon's experience" (0.281) were the paramount criteria. The results showed some variation in the ranking of criteria between the decision steps. In the second decision step, for instance, the tumor-related criterion "presence of perforation" was just the fifth. The consistency of decision support systems largely depends on the quality of the underlying decision tree. When several choices and variables have to be considered in a decision, it is very important to determine priorities. The AHP method seems to be effective for this purpose. The decision algorithm developed by this method is more realistic and will improve the quality of the decision tree. Copyright © 2012 Elsevier B.V. All rights reserved.

Prototype Tool and Focus Group Evaluation for an Advanced Trajectory-Based Operations Concept

NASA Technical Reports Server (NTRS)

Guerreiro, Nelson M.; Jones, Denise R.; Barmore, Bryan E.; Butler, Ricky W.; Hagen, George E.; Maddalon, Jeffrey M.; Ahmad, Nash'at N.

2017-01-01

Trajectory-based operations (TBO) is a key concept in the Next Generation Air Transportation System transformation of the National Airspace System (NAS) that will increase the predictability and stability of traffic flows, support a common operational picture through the use of digital data sharing, facilitate more effective collaborative decision making between airspace users and air navigation service providers, and enable increased levels of integrated automation across the NAS. NASA has been developing trajectory-based systems to improve the efficiency of the NAS during specific phases of flight and is now also exploring Advanced 4-Dimensional Trajectory (4DT) operational concepts that will integrate these technologies and incorporate new technology where needed to create both automation and procedures to support gate-to-gate TBO. A TBO Prototype simulation toolkit has been developed that demonstrates initial functionality of an Advanced 4DT TBO concept. Pilot and controller subject matter experts (SMEs) were brought to the Air Traffic Operations Laboratory at NASA Langley Research Center for discussions on an Advanced 4DT operational concept and were provided an interactive demonstration of the TBO Prototype using four example scenarios. The SMEs provided feedback on potential operational, technological, and procedural opportunities and concerns. This paper describes an Advanced 4DT operational concept, the TBO Prototype, the demonstration scenarios and methods used, and the feedback obtained from the pilot and controller SMEs in this focus group activity.

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2017-01-01

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan

PubMed Central

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Rau, Cheng-Shyuan; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-01-01

Objectives This study aimed to build and test the models of machine learning (ML) to predict the mortality of hospitalised motorcycle riders. Setting The study was conducted in a level-1 trauma centre in southern Taiwan. Participants Motorcycle riders who were hospitalised between January 2009 and December 2015 were classified into a training set (n=6306) and test set (n=946). Using the demographic information, injury characteristics and laboratory data of patients, logistic regression (LR), support vector machine (SVM) and decision tree (DT) analyses were performed to determine the mortality of individual motorcycle riders, under different conditions, using all samples or reduced samples, as well as all variables or selected features in the algorithm. Primary and secondary outcome measures The predictive performance of the model was evaluated based on accuracy, sensitivity, specificity and geometric mean, and an analysis of the area under the receiver operating characteristic curves of the two different models was carried out. Results In the training set, both LR and SVM had a significantly higher area under the receiver operating characteristic curve (AUC) than DT. No significant difference was observed in the AUC of LR and SVM, regardless of whether all samples or reduced samples and whether all variables or selected features were used. In the test set, the performance of the SVM model for all samples with selected features was better than that of all other models, with an accuracy of 98.73%, sensitivity of 86.96%, specificity of 99.02%, geometric mean of 92.79% and AUC of 0.9517, in mortality prediction. Conclusion ML can provide a feasible level of accuracy in predicting the mortality of motorcycle riders. Integration of the ML model, particularly the SVM algorithm in the trauma system, may help identify high-risk patients and, therefore, guide appropriate interventions by the clinical staff. PMID:29306885

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

PubMed

Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

2017-12-19

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

PubMed

Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

2017-10-11

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

RE-Powering’s Electronic Decision Tree

EPA Pesticide Factsheets

Developed by US EPA's RE-Powering America's Land Initiative, the RE-Powering Decision Trees tool guides interested parties through a process to screen sites for their suitability for solar photovoltaics or wind installations

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Determinants of farmers' tree planting investment decision as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, B.; Bewket, W.; Bräuning, A.

2015-11-01

Land degradation due to lack of sustainable land management practices are one of the critical challenges in many developing countries including Ethiopia. This study explores the major determinants of farm level tree planting decision as a land management strategy in a typical framing and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and binary logistic regression model. The model significantly predicted farmers' tree planting decision (Chi-square = 37.29, df = 15, P<0.001). Besides, the computed significant value of the model suggests that all the considered predictor variables jointly influenced the farmers' decision to plant trees as a land management strategy. In this regard, the finding of the study show that local land-users' willingness to adopt tree growing decision is a function of a wide range of biophysical, institutional, socioeconomic and household level factors, however, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system have positively and significantly influence on tree growing investment decisions in the study watershed. Eventually, the processes of land use conversion and land degradation are serious which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, devising sustainable and integrated land management policy options and implementing them would enhance ecological restoration and livelihood sustainability in the study watershed.

Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, Berhan; Bewket, Woldeamlak; Bräuning, Achim

2016-04-01

Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 = 37.29, df = 15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed.

Comparison of SVM RBF-NN and DT for crop and weed identification based on spectral measurement over corn fields

USDA-ARS?s Scientific Manuscript database

It is important to find an appropriate pattern-recognition method for in-field plant identification based on spectral measurement in order to classify the crop and weeds accurately. In this study, the method of Support Vector Machine (SVM) was evaluated and compared with two other methods, Decision ...

Ethnographic Decision Tree Modeling: A Research Method for Counseling Psychology.

ERIC Educational Resources Information Center

Beck, Kirk A.

2005-01-01

This article describes ethnographic decision tree modeling (EDTM; C. H. Gladwin, 1989) as a mixed method design appropriate for counseling psychology research. EDTM is introduced and located within a postpositivist research paradigm. Decision theory that informs EDTM is reviewed, and the 2 phases of EDTM are highlighted. The 1st phase, model…

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

PRIA 3 Fee Determination Decision Tree

EPA Pesticide Factsheets

The PRIA 3 decision tree will help applicants requesting a pesticide registration or certain tolerance action to accurately identify the category of their application and the amount of the required fee before they submit the application.

Solar and Wind Site Screening Decision Trees

EPA Pesticide Factsheets

EPA and NREL created a decision tree to guide state and local governments and other stakeholders through a process for screening sites for their suitability for future redevelopment with solar photovoltaic (PV) energy and wind energy.

Computer-Based Driving in Dementia Decision Tool With Mail Support: Cluster Randomized Controlled Trial.

PubMed

Rapoport, Mark J; Zucchero Sarracini, Carla; Kiss, Alex; Lee, Linda; Byszewski, Anna; Seitz, Dallas P; Vrkljan, Brenda; Molnar, Frank; Herrmann, Nathan; Tang-Wai, David F; Frank, Christopher; Henry, Blair; Pimlott, Nicholas; Masellis, Mario; Naglie, Gary

2018-05-25

Physicians often find significant challenges in assessing automobile driving in persons with mild cognitive impairment and mild dementia and deciding when to report to transportation administrators. Care must be taken to balance the safety of patients and other road users with potential negative effects of issuing such reports. The aim of this study was to assess whether a computer-based Driving in Dementia Decision Tool (DD-DT) increased appropriate reporting of patients with mild dementia or mild cognitive impairment to transportation administrators. The study used a parallel-group cluster nonblinded randomized controlled trial design to test a multifaceted knowledge translation intervention. The intervention included a computer-based decision support system activated by the physician-user, which provides a recommendation about whether to report patients with mild dementia or mild cognitive impairment to transportation administrators, based on an algorithm derived from earlier work. The intervention also included a mailed educational package and Web-based specialized reporting forms. Specialists and family physicians with expertise in dementia or care of the elderly were stratified by sex and randomized to either use the DD-DT or a control version of the tool that required identical data input as the intervention group, but instead generated a generic reminder about the reporting legislation in Ontario, Canada. The trial ran from September 9, 2014 to January 29, 2016, and the primary outcome was the number of reports made to the transportation administrators concordant with the algorithm. A total of 69 participating physicians were randomized, and 36 of these used the DD-DT; 20 of the 35 randomized to the intervention group used DD-DT with 114 patients, and 16 of the 34 randomized to the control group used it with 103 patients. The proportion of all assessed patients reported to the transportation administrators concordant with recommendation did not differ between the intervention and the control groups (50% vs 49%; Z=-0.19, P=.85). Two variables predicted algorithm-based reporting-caregiver concern (odds ratio [OR]=5.8, 95% CI 2.5-13.6, P<.001) and abnormal clock drawing (OR 6.1, 95% CI 3.1-11.8, P<.001). On the basis of this quantitative analysis, in-office abnormal clock drawing and expressions of concern about driving from caregivers substantially influenced physicians to report patients with mild dementia or mild cognitive impairment to transportation administrators, but the DD-DT tool itself did not increase such reports among these expert physicians. ClinicalTrials.gov NCT02036099; https://clinicaltrials.gov/ct2/show/NCT02036099 (Archived by WebCite at http://www.webcitation.org/6zGMF1ky8). ©Mark J Rapoport, Carla Zucchero Sarracini, Alex Kiss, Linda Lee, Anna Byszewski, Dallas P Seitz, Brenda Vrkljan, Frank Molnar, Nathan Herrmann, David F Tang-Wai, Christopher Frank, Blair Henry, Nicholas Pimlott, Mario Masellis, Gary Naglie. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.05.2018.

Automated diagnosis of dry eye using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

2015-07-01

Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device.

PubMed

Jeon, Hyoseon; Lee, Woongwoo; Park, Hyeyoung; Lee, Hong Ji; Kim, Sang Kyong; Kim, Han Byul; Jeon, Beomseok; Park, Kwang Suk

2017-09-09

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson's Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson's disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k -nearest-neighbor ( k NN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities.

PubMed

Moon, Mikyung; Lee, Soo-Kyoung

2017-01-01

The purpose of this study was to use decision tree analysis to explore the factors associated with pressure ulcers (PUs) among elderly people admitted to Korean long-term care facilities. The data were extracted from the 2014 National Inpatient Sample (NIS)-data of Health Insurance Review and Assessment Service (HIRA). A MapReduce-based program was implemented to join and filter 5 tables of the NIS. The outcome predicted by the decision tree model was the prevalence of PUs as defined by the Korean Standard Classification of Disease-7 (KCD-7; code L89 * ). Using R 3.3.1, a decision tree was generated with the finalized 15,856 cases and 830 variables. The decision tree displayed 15 subgroups with 8 variables showing 0.804 accuracy, 0.820 sensitivity, and 0.787 specificity. The most significant primary predictor of PUs was length of stay less than 0.5 day. Other predictors were the presence of an infectious wound dressing, followed by having diagnoses numbering less than 3.5 and the presence of a simple dressing. Among diagnoses, "injuries to the hip and thigh" was the top predictor ranking 5th overall. Total hospital cost exceeding 2,200,000 Korean won (US $2,000) rounded out the top 7. These results support previous studies that showed length of stay, comorbidity, and total hospital cost were associated with PUs. Moreover, wound dressings were commonly used to treat PUs. They also show that machine learning, such as a decision tree, could effectively predict PUs using big data.

Predicting the probability of mortality of gastric cancer patients using decision tree.

PubMed

Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

2015-06-01

Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

PubMed

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Correlation Between the System Capabilities Analytic Process (SCAP) and the Missions and Means Framework (MMF)

DTIC Science & Technology

2013-05-01

specifics of the correlation will be explored followed by discussion of new paradigms— the ordered event list (OEL) and the decision tree — that result from...4.2.1  Brief Overview of the Decision Tree Paradigm ................................................15  4.2.2  OEL Explained...6  Figure 3. A depiction of a notional fault/activation tree . ................................................................7

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

Space/age forestry: Implications of planting density and rotation age in SRIC management decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merriam, R.A.; Phillips, V.D.; Liu, W.

1993-12-31

Short-rotation intensive-culture (SRIC) of promising tree crops is being evaluated worldwide for the production of methanol, ethanol, and electricity from renewable biomass resources. Planting density and rotation age are fundamental management decisions associated with SRIC energy plantations. Most studies of these variables have been conducted without the benefit of a unifying theory of the effects of growing space and rotation age on individual tree growth and stand level productivity. A modeling procedure based on field trials of Eucalyptus spp. is presented that evaluates the growth potential of a tree in the absence and presence of competition of neighboring trees inmore » a stand. The results of this analysis are useful in clarifying economic implications of different growing space and rotation age decisions that tree plantation managers must make. The procedure is readily applicable to other species under consideration for SRIC plantations at any location.« less

A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities

USGS Publications Warehouse

Capel, Paul D.; Wolock, David M.; Coupe, Richard H.; Roth, Jason L.

2018-01-10

Agricultural activities can affect water quality and the health of aquatic ecosystems; many water-quality issues originate with the movement of water, agricultural chemicals, and eroded soil from agricultural areas to streams and groundwater. Most agricultural activities are designed to sustain or increase crop production, while some are designed to protect soil and water resources. Numerous soil- and water-protection practices are designed to reduce the volume and velocity of runoff and increase infiltration. This report presents a conceptual framework that combines generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities, as they relate to hydrology, to create attainable expectations for the protection of—with the goal of improving—water quality through changes in an agricultural activity.The framework presented uses two types of decision trees to guide decision making toward attainable expectations regarding the effectiveness of changing agricultural activities to protect and improve water quality in streams. One decision tree organizes decision making by considering the hydrologic setting and chemical behaviors, largely at the field scale. This decision tree can help determine which agricultural activities could effectively protect and improve water quality in a stream from the movement of chemicals, or sediment, from a field. The second decision tree is a chemical fate accounting tree. This decision tree helps set attainable expectations for the permanent removal of sediment, elements, and organic chemicals—such as herbicides and insecticides—through trapping or conservation tillage practices. Collectively, this conceptual framework consolidates diverse hydrologic settings, chemicals, and agricultural activities into a single, broad context that can be used to set attainable expectations for agricultural activities. This framework also enables better decision making for future agricultural activities as a means to reduce current, and prevent new, water-quality issues.

Rise of the Functionals?: Mobility Air Force Developmental Teams and their Impact on Officer Education and Advancement

DTIC Science & Technology

2014-06-01

Specialists (43X/44X/45X) Chaplain (52R) Civil Engineer (32E) Combat Air Force Contracting (64P) Cyber Operations (17D) Dental (47X) Finance (65 F...assume the principal has a perfect decision making calculus . The MAF DT may actually have asymmetric information, and given the opportunity, they could

Vlsi implementation of flexible architecture for decision tree classification in data mining

NASA Astrophysics Data System (ADS)

Sharma, K. Venkatesh; Shewandagn, Behailu; Bhukya, Shankar Nayak

2017-07-01

The Data mining algorithms have become vital to researchers in science, engineering, medicine, business, search and security domains. In recent years, there has been a terrific raise in the size of the data being collected and analyzed. Classification is the main difficulty faced in data mining. In a number of the solutions developed for this problem, most accepted one is Decision Tree Classification (DTC) that gives high precision while handling very large amount of data. This paper presents VLSI implementation of flexible architecture for Decision Tree classification in data mining using c4.5 algorithm.

Study on vibration characteristics and fault diagnosis method of oil-immersed flat wave reactor in Arctic area converter station

NASA Astrophysics Data System (ADS)

Lai, Wenqing; Wang, Yuandong; Li, Wenpeng; Sun, Guang; Qu, Guomin; Cui, Shigang; Li, Mengke; Wang, Yongqiang

2017-10-01

Based on long term vibration monitoring of the No.2 oil-immersed fat wave reactor in the ±500kV converter station in East Mongolia, the vibration signals in normal state and in core loose fault state were saved. Through the time-frequency analysis of the signals, the vibration characteristics of the core loose fault were obtained, and a fault diagnosis method based on the dual tree complex wavelet (DT-CWT) and support vector machine (SVM) was proposed. The vibration signals were analyzed by DT-CWT, and the energy entropy of the vibration signals were taken as the feature vector; the support vector machine was used to train and test the feature vector, and the accurate identification of the core loose fault of the flat wave reactor was realized. Through the identification of many groups of normal and core loose fault state vibration signals, the diagnostic accuracy of the result reached 97.36%. The effectiveness and accuracy of the method in the fault diagnosis of the flat wave reactor core is verified.

Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.

PubMed

Khalkhali, Hamid Reza; Lotfnezhad Afshar, Hadi; Esnaashari, Omid; Jabbari, Nasrollah

2016-01-01

Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already. The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity. The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively. The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.

Comparing MODIS C6 'Deep Blue' and 'Dark Target' Aerosol Data

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Sayer, A. M.; Bettenhausen, C.; Lee, J.; Levy, R. C.; Mattoo, S.; Munchak, L. A.; Kleidman, R.

2014-01-01

The MODIS Collection 6 Atmospheres product suite includes refined versions of both 'Deep Blue' (DB) and 'Dark Target' (DT) aerosol algorithms, with the DB dataset now expanded to include coverage over vegetated land surfaces. This means that, over much of the global land surface, users will have both DB and DT data to choose from. A 'merged' dataset is also provided, primarily for visualization purposes, which takes retrievals from either or both algorithms based on regional and seasonal climatologies of normalized difference vegetation index (NDVI). This poster present some comparisons of these two C6 aerosol algorithms, focusing on AOD at 550 nm derived from MODIS Aqua measurements, with each other and with Aerosol Robotic Network (AERONET) data, with the intent to facilitate user decisions about the suitability of the two datasets for their desired applications.

The Utility of Decision Trees in Oncofertility Care in Japan.

PubMed

Ito, Yuki; Shiraishi, Eriko; Kato, Atsuko; Haino, Takayuki; Sugimoto, Kouhei; Okamoto, Aikou; Suzuki, Nao

2017-03-01

To identify the utility and issues associated with the use of decision trees in oncofertility patient care in Japan. A total of 35 women who had been diagnosed with cancer, but had not begun anticancer treatment, were enrolled. We applied the oncofertility decision tree for women published by Gardino et al. to counsel a consecutive series of women on fertility preservation (FP) options following cancer diagnosis. Percentage of women who decided to undergo oocyte retrieval for embryo cryopreservation and the expected live-birth rate for these patients were calculated using the following equation: expected live-birth rate = pregnancy rate at each age per embryo transfer × (1 - miscarriage rate) × No. of cryopreserved embryos. Oocyte retrieval was performed for 17 patients (48.6%; mean ± standard deviation [SD] age, 36.35 ± 3.82 years). The mean ± SD number of cryopreserved embryos was 5.29 ± 4.63. The expected live-birth rate was 0.66. The expected live-birth rate with FP indicated that one in three oncofertility patients would not expect to have a live birth following oocyte retrieval and embryo cryopreservation. While the decision trees were useful as decision-making tools for women contemplating FP, in the context of the current restrictions on oocyte donation and the extremely small number of adoptions in Japan, the remaining options for fertility after cancer are limited. In order for cancer survivors to feel secure in their decisions, the decision tree may need to be adapted simultaneously with improvements to the social environment, such as greater support for adoption.

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

The application of a decision tree to establish the parameters associated with hypertension.

PubMed

Tayefi, Maryam; Esmaeili, Habibollah; Saberi Karimian, Maryam; Amirabadi Zadeh, Alireza; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A; Ghayour-Mobarhan, Majid

2017-02-01

Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Identifying the performance characteristics of a winning outcome in elite mixed martial arts competition.

PubMed

James, Lachlan P; Robertson, Sam; Haff, G Gregory; Beckman, Emma M; Kelly, Vincent G

2017-03-01

To determine those performance indicators that have the greatest influence on classifying outcome at the elite level of mixed martial arts (MMA). A secondary objective was to establish the efficacy of decision tree analysis in explaining the characteristics of victory when compared to alternate statistical methods. Cross-sectional observational. Eleven raw performance indicators from male Ultimate Fighting Championship bouts (n=234) from July 2014 to December 2014 were screened for analysis. Each raw performance indicator was also converted to a rate-dependent measure to be scaled to fight duration. Further, three additional performance indicators were calculated from the dataset and included in the analysis. Cohen's d effect sizes were employed to determine the magnitude of the differences between Wins and Losses, while decision tree (chi-square automatic interaction detector (CHAID)) and discriminant function analyses (DFA) were used to classify outcome (Win and Loss). Effect size comparisons revealed differences between Wins and Losses across a number of performance indicators. Decision tree (raw: 71.8%; rate-scaled: 76.3%) and DFA (raw: 71.4%; rate-scaled 71.2%) achieved similar classification accuracies. Grappling and accuracy performance indicators were the most influential in explaining outcome. The decision tree models also revealed multiple combinations of performance indicators leading to victory. The decision tree analyses suggest that grappling activity and technique accuracy are of particular importance in achieving victory in elite-level MMA competition. The DFA results supported the importance of these performance indicators. Decision tree induction represents an intuitive and slightly more accurate approach to explaining bout outcome in this sport when compared to DFA. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Faults Discovery By Using Mined Data

NASA Technical Reports Server (NTRS)

Lee, Charles

2005-01-01

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Evaluation with Decision Trees of Efficacy and Safety of Semirigid Ureteroscopy in the Treatment of Proximal Ureteral Calculi.

PubMed

Sancak, Eyup Burak; Kılınç, Muhammet Fatih; Yücebaş, Sait Can

2017-01-01

The decision on the choice of proximal ureteral stone therapy depends on many factors, and sometimes urologists have difficulty in choosing the treatment option. This study is aimed at evaluating the factors affecting the success of semirigid ureterorenoscopy (URS) using the "decision tree" method. From January 2005 to November 2015, the data of consecutive patients treated for proximal ureteral stone were retrospectively analyzed. A total of 920 patients with proximal ureteral stone treated with semirigid URS were included in the study. All statistically significant attributes were tested using the decision tree method. The model created using decision tree had a sensitivity of 0.993 and an accuracy of 0.857. While URS treatment was successful in 752 patients (81.7%), it was unsuccessful in 168 patients (18.3%). According to the decision tree method, the most important factor affecting the success of URS is whether the stone is impacted to the ureteral wall. The second most important factor affecting treatment was intramural stricture requiring dilatation if the stone is impacted, and the size of the stone if not impacted. Our study suggests that the impacted stone, intramural stricture requiring dilatation and stone size may have a significant effect on the success rate of semirigid URS for proximal ureteral stone. Further studies with population-based and longitudinal design should be conducted to confirm this finding. © 2017 S. Karger AG, Basel.

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

Planning effectiveness may grow on fault trees.

PubMed

Chow, C W; Haddad, K; Mannino, B

1991-10-01

The first step of a strategic planning process--identifying and analyzing threats and opportunities--requires subjective judgments. By using an analytical tool known as a fault tree, healthcare administrators can reduce the unreliability of subjective decision making by creating a logical structure for problem solving and decision making. A case study of 11 healthcare administrators showed that an analysis technique called prospective hindsight can add to a fault tree's ability to improve a strategic planning process.

Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory

NASA Astrophysics Data System (ADS)

Grasinger, M.; O'Malley, D.; Vesselinov, V. V.; Karra, S.

2015-12-01

Carbon capture and sequestration has the potential to reduce greenhouse gasemissions. However, care must be taken when choosing a site for CO2 seques-tration to ensure that the CO2 remains sequestered for many years, and thatthe environment is not harmed in any way. Making a rational decision be-tween potential sites for sequestration is not without its challenges because, asin the case of many environmental and subsurface problems, there is a lot ofuncertainty that exists. A method for making decisions under various typesand severities of uncertainty, Bayesian-Information-Gap Decision Theory (BIGDT), is presented. BIG DT was coupled with a numerical model for CO2 wellinjection and the resulting framework was then applied to a problem of selectingbetween two potential sites for CO2 sequestration. The results of the analysisare presented, followed by a discussion of the decision process.

Prescriptive models to support decision making in genetics.

PubMed

Pauker, S G; Pauker, S P

1987-01-01

Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.

Applications of urban tree canopy assessment and prioritization tools: supporting collaborative decision making to achieve urban sustainability goals

Treesearch

Dexter H. Locke; J. Morgan Grove; Michael Galvin; Jarlath P.M. ONeil-Dunne; Charles Murphy

2013-01-01

Urban Tree Canopy (UTC) Prioritizations can be both a set of geographic analysis tools and a planning process for collaborative decision-making. In this paper, we describe how UTC Prioritizations can be used as a planning process to provide decision support to multiple government agencies, civic groups and private businesses to aid in reaching a canopy target. Linkages...

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

New Splitting Criteria for Decision Trees in Stationary Data Streams.

PubMed

Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

2018-06-01

The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.

PubMed

Tanaka, Tomohiro; Voigt, Michael D

2018-03-01

Non-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients. We performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984). NMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI < 40 who did not receive sirolimus, as high risk (7.3% cumulative incidence of NMSC). The predictions in the derivation set were almost identical to those in the validation set (r 2  = 0.971, p < 0.0001). Cumulative incidence of NMSC in low, moderate and high risk groups at 5/10/20 year was 0.5/1.2/3.3, 2.1/4.8/11.7 and 5.6/11.6/23.1% (p < 0.0001). The decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.

Interpretation of diagnostic data: 6. How to do it with more complex maths.

PubMed

1983-11-15

We have now shown you how to use decision analysis in making those rare, tough diagnostic decisions that are not soluble through other, easier routes. In summary, to "use more complex maths" the following steps will be useful: Create a decision tree or map of all the pertinent courses of action and their consequences. Assign probabilities to the branches of each chance node. Assign utilities to each of the potential outcomes shown on the decision tree. Combine the probabilities and utilities for each node on the decision tree. Pick the decision that leads to the highest expected utility. Test your decision for its sensitivity to clinically sensible changes in probabilities and utilities. That concludes this series of clinical epidemiology rounds. You've come a long way from "doing it with pictures" and are now able to extract most of the diagnostic information that can be provided from signs, symptoms and laboratory investigations. We would appreciate learning whether you have found this series useful and how we can do a better job of presenting these and other elements of "the science of the art of medicine".

Policy Route Map for Academic Libraries' Digital Content

ERIC Educational Resources Information Center

Koulouris, Alexandros; Kapidakis, Sarantos

2012-01-01

This paper presents a policy decision tree for digital information management in academic libraries. The decision tree is a policy guide, which offers alternative access and reproduction policy solutions according to the prevailing circumstances (for example acquisition method, copyright ownership). It refers to the digital information life cycle,…

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Siting a municipal solid waste disposal facility, part II: the effects of external criteria on the final decision.

PubMed

Korucu, M Kemal; Karademir, Aykan

2014-02-01

The procedure of a multi-criteria decision analysis supported by the geographic information systems was applied to the site selection process of a planning municipal solid waste management practice based on twelve different scenarios. The scenarios included two different decision tree modes and two different weighting models for three different area requirements. The suitability rankings of the suitable sites obtained from the application of the decision procedure for the scenarios were assessed by a factorial experimental design concerning the effect of some external criteria on the final decision of the site selection process. The external criteria used in the factorial experimental design were defined as "Risk perception and approval of stakeholders" and "Visibility". The effects of the presence of these criteria in the decision trees were evaluated in detail. For a quantitative expression of the differentiations observed in the suitability rankings, the ranking data were subjected to ANOVA test after a normalization process. Then the results of these tests were evaluated by Tukey test to measure the effects of external criteria on the final decision. The results of Tukey tests indicated that the involvement of the external criteria into the decision trees produced statistically meaningful differentiations in the suitability rankings. Since the external criteria could cause considerable external costs during the operation of the disposal facilities, the presence of these criteria in the decision tree in addition to the other criteria related to environmental and legislative requisites could prevent subsequent external costs in the first place.

Decision support for mitigating the risk of tree induced transmission line failure in utility rights-of-way.

PubMed

Poulos, H M; Camp, A E

2010-02-01

Vegetation management is a critical component of rights-of-way (ROW) maintenance for preventing electrical outages and safety hazards resulting from tree contact with conductors during storms. Northeast Utility's (NU) transmission lines are a critical element of the nation's power grid; NU is therefore under scrutiny from federal agencies charged with protecting the electrical transmission infrastructure of the United States. We developed a decision support system to focus right-of-way maintenance and minimize the potential for a tree fall episode that disables transmission capacity across the state of Connecticut. We used field data on tree characteristics to develop a system for identifying hazard trees (HTs) in the field using limited equipment to manage Connecticut power line ROW. Results from this study indicated that the tree height-to-diameter ratio, total tree height, and live crown ratio were the key characteristics that differentiated potential risk trees (danger trees) from trees with a high probability of tree fall (HTs). Products from this research can be transferred to adaptive right-of-way management, and the methods we used have great potential for future application to other regions of the United States and elsewhere where tree failure can disrupt electrical power.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

2004-01-01

To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT.

PubMed

Pak, Kyoungjune; Kim, Keunyoung; Kim, Mi-Hyun; Eom, Jung Seop; Lee, Min Ki; Cho, Jeong Su; Kim, Yun Seong; Kim, Bum Soo; Kim, Seong Jang; Kim, In Joo

2018-01-01

We aimed to develop a decision tree model to improve diagnostic performance of positron emission tomography/computed tomography (PET/CT) to detect metastatic lymph nodes (LN) in non-small cell lung cancer (NSCLC). 115 patients with NSCLC were included in this study. The training dataset included 66 patients. A decision tree model was developed with 9 variables, and validated with 49 patients: short and long diameters of LNs, ratio of short and long diameters, maximum standardized uptake value (SUVmax) of LN, mean hounsfield unit, ratio of LN SUVmax and ascending aorta SUVmax (LN/AA), and ratio of LN SUVmax and superior vena cava SUVmax. A total of 301 LNs of 115 patients were evaluated in this study. Nodular calcification was applied as the initial imaging parameter, and LN SUVmax (≥3.95) was assessed as the second. LN/AA (≥2.92) was required to high LN SUVmax. Sensitivity was 50% for training dataset, and 40% for validation dataset. However, specificity was 99.28% for training dataset, and 96.23% for validation dataset. In conclusion, we have developed a new decision tree model for interpreting mediastinal LNs. All LNs with nodular calcification were benign, and LNs with high LN SUVmax and high LN/AA were metastatic Further studies are needed to incorporate subjective parameters and pathologic evaluations into a decision tree model to improve the test performance of PET/CT.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Fish to meat intake ratio and cooking oils are associated with hepatitis C virus carriers with persistently normal alanine aminotransferase levels.

PubMed

Otsuka, Momoka; Uchida, Yuki; Kawaguchi, Takumi; Taniguchi, Eitaro; Kawaguchi, Atsushi; Kitani, Shingo; Itou, Minoru; Oriishi, Tetsuharu; Kakuma, Tatsuyuki; Tanaka, Suiko; Yagi, Minoru; Sata, Michio

2012-10-01

  Dietary habits are involved in the development of chronic inflammation; however, the impact of dietary profiles of hepatitis C virus carriers with persistently normal alanine transaminase levels (HCV-PNALT) remains unclear. The decision-tree algorithm is a data-mining statistical technique, which uncovers meaningful profiles of factors from a data collection. We aimed to investigate dietary profiles associated with HCV-PNALT using a decision-tree algorithm.   Twenty-seven HCV-PNALT and 41 patients with chronic hepatitis C were enrolled in this study. Dietary habit was assessed using a validated semiquantitative food frequency questionnaire. A decision-tree algorithm was created by dietary variables, and was evaluated by area under the receiver operating characteristic curve analysis (AUROC).   In multivariate analysis, fish to meat ratio, dairy product and cooking oils were identified as independent variables associated with HCV-PNALT. The decision-tree algorithm was created with two variables: a fish to meat ratio and cooking oils/ideal bodyweight. When subjects showed a fish to meat ratio of 1.24 or more, 68.8% of the subjects were HCV-PNALT. On the other hand, 11.5% of the subjects were HCV-PNALT when subjects showed a fish to meat ratio of less than 1.24 and cooking oil/ideal bodyweight of less than 0.23 g/kg. The difference in the proportion of HCV-PNALT between these groups are significant (odds ratio 16.87, 95% CI 3.40-83.67, P = 0.0005). Fivefold cross-validation of the decision-tree algorithm showed an AUROC of 0.6947 (95% CI 0.5656-0.8238, P = 0.0067).   The decision-tree algorithm disclosed that fish to meat ratio and cooking oil/ideal bodyweight were associated with HCV-PNALT. © 2012 The Japan Society of Hepatology.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Computerization of guidelines: a knowledge specification method to convert text to detailed decision tree for electronic implementation.

PubMed

Aguirre-Junco, Angel-Ricardo; Colombet, Isabelle; Zunino, Sylvain; Jaulent, Marie-Christine; Leneveut, Laurence; Chatellier, Gilles

2004-01-01

The initial step for the computerization of guidelines is the knowledge specification from the prose text of guidelines. We describe a method of knowledge specification based on a structured and systematic analysis of text allowing detailed specification of a decision tree. We use decision tables to validate the decision algorithm and decision trees to specify and represent this algorithm, along with elementary messages of recommendation. Edition tools are also necessary to facilitate the process of validation and workflow between expert physicians who will validate the specified knowledge and computer scientist who will encode the specified knowledge in a guide-line model. Applied to eleven different guidelines issued by an official agency, the method allows a quick and valid computerization and integration in a larger decision support system called EsPeR (Personalized Estimate of Risks). The quality of the text guidelines is however still to be developed further. The method used for computerization could help to define a framework usable at the initial step of guideline development in order to produce guidelines ready for electronic implementation.

Hydrodistillation extraction time effect on essential oil yield, composition, and bioactivity of coriander oil.

PubMed

Zheljazkov, Valtcho D; Astatkie, Tess; Schlegel, Vicki

2014-01-01

Coriander (Coriandrum sativum L.) is a major essential oil crop grown throughout the world. Coriander essential oil is extracted from coriander fruits via hydrodistillation, with the industry using 180-240 min of distillation time (DT), but the optimum DT for maximizing essential oil yield, composition of constituents, and antioxidant activities are not known. This research was conducted to determine the effect of DT on coriander oil yield, composition, and bioactivity. The results show that essential oil yield at the shorter DT was low and generally increased with increasing DT with the maximum yields achieved at DT between 40 and 160 min. The concentrations of the low-boiling point essential oil constituents: α-pinene, camphene, β-pinene, myrcene, para-cymene, limonene, and γ-terpinene were higher at shorter DT (< 2.5 min) and decreased with increasing DT; but the trend reversed for the high-boiling point constituents: geraniol and geranyl-acetate. The concentration of the major essential oil constituent, linalool, was 51% at DT 1.15 min, and increased steadily to 68% with increasing DT. In conclusion, 40 min DT is sufficient to maximize yield of essential oil; and different DT can be used to obtain essential oil with differential composition. Its antioxidant capacity was affected by the DT, with 20 and 240 min DT showing higher antioxidant activity. Comparisons of coriander essential oil composition must consider the length of the DT.

Rice growth monitoring using simulated compact polarimetric C band SAR

NASA Astrophysics Data System (ADS)

Yang, Zhi; Li, Kun; Liu, Long; Shao, Yun; Brisco, Brian; Li, Weiguo

2014-12-01

In this study, a set of nine compact polarimetric (CP) images were simulated from polarimetric RADARSAT-2 data acquired over a test site containing two types of rice field in Jiangsu province, China. The types of rice field in the test site were (1) transplanted hybrid rice fields, and (2) direct-sown japonica rice fields. Both types have different yields and phenological stages. As a first step, the two types of rice field were distinguished with 94% and 86% accuracy respectively through analyzing CP synthetic aperture radar (SAR) observations and their behavior in terms of scattering mechanisms during the rice growth season. The focus was then on phenology retrieval for each type of rice field. A decision tree (DT) algorithm was built to fulfill the precise retrieval of rice phenological stages, in which seven phenological stages were discriminated. The key criterion for each phenological stage was composed of 1-4 CP parameters, some of which were first used for rice phenology retrieval and found to be very sensitive to rice phenological changes. The retrieval results were verified at parcel level for a set of 12 stands of rice and up to nine observation dates per stand. This gave an accuracy of 88-95%. Throughout the phenology retrieval process, only simulated CP data were used, without any auxiliary data. These results demonstrate the potential of CP SAR for rice growth monitoring applications.

Validating a decision tree for serious infection: diagnostic accuracy in acutely ill children in ambulatory care.

PubMed

Verbakel, Jan Y; Lemiengre, Marieke B; De Burghgraeve, Tine; De Sutter, An; Aertgeerts, Bert; Bullens, Dominique M A; Shinkins, Bethany; Van den Bruel, Ann; Buntinx, Frank

2015-08-07

Acute infection is the most common presentation of children in primary care with only few having a serious infection (eg, sepsis, meningitis, pneumonia). To avoid complications or death, early recognition and adequate referral are essential. Clinical prediction rules have the potential to improve diagnostic decision-making for rare but serious conditions. In this study, we aimed to validate a recently developed decision tree in a new but similar population. Diagnostic accuracy study validating a clinical prediction rule. Acutely ill children presenting to ambulatory care in Flanders, Belgium, consisting of general practice and paediatric assessment in outpatient clinics or the emergency department. Physicians were asked to score the decision tree in every child. The outcome of interest was hospital admission for at least 24 h with a serious infection within 5 days after initial presentation. We report the diagnostic accuracy of the decision tree in sensitivity, specificity, likelihood ratios and predictive values. In total, 8962 acute illness episodes were included, of which 283 lead to admission to hospital with a serious infection. Sensitivity of the decision tree was 100% (95% CI 71.5% to 100%) at a specificity of 83.6% (95% CI 82.3% to 84.9%) in the general practitioner setting with 17% of children testing positive. In the paediatric outpatient and emergency department setting, sensitivities were below 92%, with specificities below 44.8%. In an independent validation cohort, this clinical prediction rule has shown to be extremely sensitive to identify children at risk of hospital admission for a serious infection in general practice, making it suitable for ruling out. NCT02024282. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Decay fungi of oaks and associated hardwoods for western arborists

Treesearch

Jessie A. Glaeser; Kevin T. Smith

2010-01-01

Examination of trees for the presence and extent of decay should be part of any hazard tree assessment. Identification of the fungi responsible for the decay improves prediction of tree performance and the quality of management decisions, including tree pruning or removal. Scouting for Sudden Oak Death (SOD) in the West has drawn attention to hardwood tree species,...

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty.

PubMed

Cheaib, Alissar; Badeau, Vincent; Boe, Julien; Chuine, Isabelle; Delire, Christine; Dufrêne, Eric; François, Christophe; Gritti, Emmanuel S; Legay, Myriam; Pagé, Christian; Thuiller, Wilfried; Viovy, Nicolas; Leadley, Paul

2012-06-01

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion. © 2012 Blackwell Publishing Ltd/CNRS.

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Treesearch

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

Which Types of Leadership Styles Do Followers Prefer? A Decision Tree Approach

ERIC Educational Resources Information Center

Salehzadeh, Reza

2017-01-01

Purpose: The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers' preferences using the decision tree technique. Design/methodology/approach: Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680…

The Americans with Disabilities Act: A Decision Tree for Social Services Administrators

ERIC Educational Resources Information Center

O'Brien, Gerald V.; Ellegood, Christina

2005-01-01

The 1990 Americans with Disabilities Act has had a profound influence on social workers and social services administrators in virtually all work settings. Because of the multiple elements of the act, however, assessing the validity of claims can be a somewhat arduous and complicated task. This article provides a "decision tree" for…

A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

2010-01-01

A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

A decision tree approach using silvics to guide planning for forest restoration

Treesearch

Sharon M. Hermann; John S. Kush; John C. Gilbert

2013-01-01

We created a decision tree based on silvics of longleaf pine (Pinus palustris) and historical descriptions to develop approaches for restoration management at Horseshoe Bend National Military Park located in central Alabama. A National Park Service goal is to promote structure and composition of a forest that likely surrounded the 1814 battlefield....

What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

Distillation time modifies essential oil yield, composition, and antioxidant capacity of fennel (Foeniculum vulgare Mill).

PubMed

Zheljazkov, Valtcho D; Horgan, Thomas; Astatkie, Tess; Schlegel, Vicki

2013-01-01

Fennel (Foeniculum vulgare Mill) is an essential oil crop grown worldwide for production of essential oil, as medicinal or as culinary herb. The essential oil is extracted via steam distillation either from the whole aboveground biomass (herb) or from fennel fruits (seed). The hypothesis of this study was that distillation time (DT) can modify fennel oil yield, composition, and antioxidant capacity of the oil. Therefore, the objective of this study was to evaluate the effect of eight DT (1.25, 2.5, 5, 10, 20, 40, 80, and 160 min) on fennel herb essential oil. Fennel essential oil yield (content) reached a maximum of 0.68% at 160 min DT. The concentration of trans-anethole (32.6-59.4% range in the oil) was low at 1.25 min DT, and increased with an increase of the DT. Alpha-phelandrene (0.9-10.5% range) was the lowest at 1.25 min DT and higher at 10, 80, and 160 min DT. Alpha-pinene (7.1-12.4% range) and beta-pinene (0.95-1.64% range) were higher in the shortest DT and the lowest at 80 min DT. Myrcene (0.93-1.95% range), delta-3-carene (2.1-3.7% range), cis-ocimene (0-0.23% range), and gamma-terpinene (0.22-2.67% range) were the lowest at 1.25 min DT and the highest at 160 min DT. In contrast, the concentrations of paracymene (0.68-5.97% range), fenchone (9.8-22.7% range), camphor (0.21-0.51% range), and cis-anethole (0.14-4.66% range) were highest at shorter DT (1.25-5 min DT) and the lowest at the longer DT (80-160 min DT). Fennel oils from the 20 and 160 min DT had higher antioxidant capacity than the fennel oil obtained at 1.25 min DT. DT can be used to obtain fennel essential oil with differential composition. DT must be reported when reporting essential oil content and composition of fennel essential oil. The results from this study may be used to compare reports in which different DT to extract essential oil from fennel biomass were used.

Evaluation of left ventricular function in anesthetized patients using femoral artery dP/dt(max).

PubMed

De Hert, Stefan G; Robert, Dominique; Cromheecke, Stefanie; Michard, Frédéric; Nijs, Jan; Rodrigus, Inez E

2006-06-01

The purpose of this study was to compare dP/dt(max) estimated from a femoral artery pressure tracing to left ventricular (LV) dP/dt(max) during various alterations in myocardial loading and contractile function. Seventy patients scheduled for elective coronary artery bypass surgery. All patients were instrumented with a high-fidelity LV catheter, a pulmonary artery catheter, and a femoral arterial catheter. In 40 patients, hemodynamic measurements were performed before and after passive leg raising and before and after calcium administration (5 mg/kg); and in 30 other patients, hemodynamic measurements were performed before and after dobutamine infusion (5 microg/kg/min over 10 minutes). LV and femoral dP/dt(max) were significantly correlated (r = 0.82, p < 0.001), but femoral dP/dt(max) systematically underestimated LV dP/dt(max) (bias = -361 +/- 96 mmHg/s). Passive leg raising induced significant increases in central venous pressure and LV end-diastolic pressure, but femoral dP/dt(max), stroke volume, and LV dP/dt(max) remained unaltered. Calcium administration induced significant and marked increases in LV dP/dt(max) (23% +/- 9%) and femoral dP/dt(max) (37% +/- 14%) associated with a significant increase in stroke volume (9% +/- 2%). Dobutamine infusion also induced significant and marked increases in LV dP/dt(max) (25% +/- 8%) and femoral dP/dt(max) (35% +/- 12%) associated with a significant increase in stroke volume (14% +/- 3%). Overall, a very close linear relationship (r = 0.93) and a good agreement (bias = -5 +/- 17 mmHg/s) were found between changes in LV dP/dt(max) and changes in femoral dP/dt(max). A very close relationship was also observed between changes in LV dP/dt(max) and changes in femoral dP/dt(max) during each intervention (leg raising, calcium administration, and dobutamine infusion). Femoral dP/dt(max) underestimated LV dP/dt(max), but changes in femoral dP/dt(max) accurately reflected changes in LV dP/dt(max) during various interventions.

Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality

PubMed Central

Vergara, Pablo M.; Soto, Gerardo E.; Rodewald, Amanda D.; Meneses, Luis O.; Pérez-Hernández, Christian G.

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox’s proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales. PMID:27416115

Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality.

PubMed

Vergara, Pablo M; Soto, Gerardo E; Moreira-Arce, Darío; Rodewald, Amanda D; Meneses, Luis O; Pérez-Hernández, Christian G

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox's proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kupriyanov, M. S., E-mail: mikhail.kupriyanov@gmail.com; Shukeilo, E. Y., E-mail: eyshukeylo@gmail.com; Shichkina, J. A., E-mail: strange.y@mail.ru

2015-11-17

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient’s health condition using data from a wearable device considers in this article.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

NASA Astrophysics Data System (ADS)

Kupriyanov, M. S.; Shukeilo, E. Y.; Shichkina, J. A.

2015-11-01

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient's health condition using data from a wearable device considers in this article.

Protein attributes contribute to halo-stability, bioinformatics approach

PubMed Central

2011-01-01

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins. PMID:21592393

Classification tree for the assessment of sedentary lifestyle among hypertensive.

PubMed

Castelo Guedes Martins, Larissa; Venícios de Oliveira Lopes, Marcos; Gomes Guedes, Nirla; Paixão de Menezes, Angélica; de Oliveira Farias, Odaleia; Alves Dos Santos, Naftale

2016-04-01

To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity

NASA Technical Reports Server (NTRS)

Tian, Jianhui; Porter, Adam; Zelkowitz, Marvin V.

1992-01-01

Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their share of problems. A decision tree model was used to identify such modules. In this current paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement was tested using data from the NASA Software Engineering Laboratory.

A key for the Forest Service hardwood tree grades

Treesearch

Gary W. Miller; Leland F. Hanks; Harry V., Jr. Wiant

1986-01-01

A dichotomous key organizes the USDA Forest Service hardwood tree grade specifications into a stepwise procedure for those learning to grade hardwood sawtimber. The key addresses the major grade factors, tree size, surface characteristics, and allowable cull deductions in a series of paried choices that lead the user to a decision regarding tree grade.

Inferences from growing trees backwards

Treesearch

David W. Green; Kent A. McDonald

1997-01-01

The objective of this paper is to illustrate how longitudinal stress wave techniques can be useful in tracking the future quality of a growing tree. Monitoring the quality of selected trees in a plantation forest could provide early input to decisions on the effectiveness of management practices, or future utilization options, for trees in a plantation. There will...

Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.

PubMed

Morales, Susana; Barros, Jorge; Echávarri, Orietta; García, Fabián; Osses, Alex; Moya, Claudia; Maino, María Paz; Fischman, Ronit; Núñez, Catalina; Szmulewicz, Tita; Tomicic, Alemka

2017-01-01

In efforts to develop reliable methods to detect the likelihood of impending suicidal behaviors, we have proposed the following. To gain a deeper understanding of the state of suicide risk by determining the combination of variables that distinguishes between groups with and without suicide risk. A study involving 707 patients consulting for mental health issues in three health centers in Greater Santiago, Chile. Using 345 variables, an analysis was carried out with artificial intelligence tools, Cross Industry Standard Process for Data Mining processes, and decision tree techniques. The basic algorithm was top-down, and the most suitable division produced by the tree was selected by using the lowest Gini index as a criterion and by looping it until the condition of belonging to the group with suicidal behavior was fulfilled. Four trees distinguishing the groups were obtained, of which the elements of one were analyzed in greater detail, since this tree included both clinical and personality variables. This specific tree consists of six nodes without suicide risk and eight nodes with suicide risk (tree decision 01, accuracy 0.674, precision 0.652, recall 0.678, specificity 0.670, F measure 0.665, receiver operating characteristic (ROC) area under the curve (AUC) 73.35%; tree decision 02, accuracy 0.669, precision 0.642, recall 0.694, specificity 0.647, F measure 0.667, ROC AUC 68.91%; tree decision 03, accuracy 0.681, precision 0.675, recall 0.638, specificity 0.721, F measure, 0.656, ROC AUC 65.86%; tree decision 04, accuracy 0.714, precision 0.734, recall 0.628, specificity 0.792, F measure 0.677, ROC AUC 58.85%). This study defines the interactions among a group of variables associated with suicidal ideation and behavior. By using these variables, it may be possible to create a quick and easy-to-use tool. As such, psychotherapeutic interventions could be designed to mitigate the impact of these variables on the emotional state of individuals, thereby reducing eventual risk of suicide. Such interventions may reinforce psychological well-being, feelings of self-worth, and reasons for living, for each individual in certain groups of patients.

Analytical and CASE study on Limited Search, ID3, CHAID, C4.5, Improved C4.5 and OVA Decision Tree Algorithms to design Decision Support System

NASA Astrophysics Data System (ADS)

Kaur, Parneet; Singh, Sukhwinder; Garg, Sushil; Harmanpreet

2010-11-01

In this paper we study about classification algorithms for farm DSS. By applying classification algorithms i.e. Limited search, ID3, CHAID, C4.5, Improved C4.5 and One VS all Decision Tree on common data set of crop with specified class, results are obtained. The tool used to derive results is SPINA. The graphical results obtained from tool are compared to suggest best technique to develop farm Decision Support System. This analysis would help to researchers to design effective and fast DSS for farmer to take decision for enhancing their yield.

6th Annual CMMI Technology Conference and User Group

DTIC Science & Technology

2006-11-17

Operationally Oriented; Customer Focused Proven Approach – Level of Detail Beginner Decision Table (DT) is a tabular representation with tailoring options to...written to reflect the experience of the author Software Engineering led the process charge in the ’80s – Used Flowcharts – CASE tools – “data...Postpo ned PCR. Verification Steps • EPG configuration audits • EPG configuration status reports Flowcharts and Entry, Task, Verification and eXit

Framework for Analytic Cognition (FAC): A Guide for Doing All-Source Intelligence Analysis

DTIC Science & Technology

2011-12-01

humans as rational decision makers has been thoroughly discounted in the last decade. Recent research in neuroscience and cognitive psychology has...Intelligence and Counterintelligence, Vol. 18, No. 2, 2005, p. 206. 60 Moore, D.T. & Krizan, L. "Intelligence Analysis: Does NSA have what it Takes...SIGINT NSA Online TS/SCI Online Digital Yes COMINT Internet None N/A Unclassified Online Digital Yes Open Source STRATFOR Local information

Surface silylation of natural mesoporous/macroporous diatomite for adsorption of benzene.

PubMed

Yu, Wenbin; Deng, Liangliang; Yuan, Peng; Liu, Dong; Yuan, Weiwei; Liu, Peng; He, Hongping; Li, Zhaohui; Chen, Fanrong

2015-06-15

Naturally occurring porous diatomite (Dt) was functionalized with phenyltriethoxysilane (PTES), and the PTES-modified diatomite (PTES-Dt) was characterized using diffuse reflectance Fourier transform infrared spectroscopy, nitrogen adsorption, nuclear magnetic resonance spectroscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis. After silylation, a functional group (-C6H5, phenyl) was successfully introduced onto the surface of Dt. PTES-Dt exhibited hydrophobic properties with a water contact angle (WCA) as high as 120°±1°, whereas Dt was superhydrophilic with a WCA of 0°. The benzene adsorption data on both Dt and PTES-Dt fit well with the Langmuir isotherm equation. The Langmuir adsorption capacity of benzene on PTES-Dt is 28.1 mg/g, more than 4-fold greater than that on Dt. Moreover, the adsorption kinetics results show that equilibrium was achieved faster for PTES-Dt than for Dt, over the relative pressure range of 0.118-0.157. The excellent benzene adsorption performance of PTES-Dt is attributed to strong π-system interactions between the phenyl groups and the benzene molecules as well as to the macroporosity of the PTES-Dt. These results show that the silylated diatomite could be a new and inexpensive adsorbent suitable for use in benzene emission control. Copyright © 2015 Elsevier Inc. All rights reserved.

Uninjured trees - a meaningful guide to white-pine weevil control decisions

Treesearch

William E. Waters

1962-01-01

The white-pine weevil, Pissodes strobi, is a particularly insidious forest pest that can render a stand of host trees virtually worthless. It rarely, if ever, kills a tree; but the crooks, forks, and internal defects that develop in attacked trees over a period of years may reduce the merchantable volume and value of the tree at harvest age to zero. Dollar losses are...

Compensatory value of urban trees in the United States

Treesearch

David J. Nowak; Daniel E. Crane; John F. Dwyer

2002-01-01

Understanding the value of an urban forest can give decision makers a better foundation for urban tree namagement. Based on tree-valuation methods of the Council of Tree and Landscape Appraisers and field data from eight cities, total compensatory value of tree populations in U.S. cities ranges from $101 million in Jersey City, New Jersey, to $6.2 billion in New York,...

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

PubMed

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Prognostic Factors and Decision Tree for Long-term Survival in Metastatic Uveal Melanoma.

PubMed

Lorenzo, Daniel; Ochoa, María; Piulats, Josep Maria; Gutiérrez, Cristina; Arias, Luis; Català, Jaum; Grau, María; Peñafiel, Judith; Cobos, Estefanía; Garcia-Bru, Pere; Rubio, Marcos Javier; Padrón-Pérez, Noel; Dias, Bruno; Pera, Joan; Caminal, Josep Maria

2017-12-04

The purpose of this study was to demonstrate the existence of a bimodal survival pattern in metastatic uveal melanoma. Secondary aims were to identify the characteristics and prognostic factors associated with long-term survival and to develop a clinical decision tree. The medical records of 99 metastatic uveal melanoma patients were retrospectively reviewed. Patients were classified as either short (≤ 12 months) or long-term survivors (> 12 months) based on a graphical interpretation of the survival curve after diagnosis of the first metastatic lesion. Ophthalmic and oncological characteristics were assessed in both groups. Of the 99 patients, 62 (62.6%) were classified as short-term survivors, and 37 (37.4%) as long-term survivors. The multivariate analysis identified the following predictors of long-term survival: age ≤ 65 years (p=0.012) and unaltered serum lactate dehydrogenase levels (p=0.018); additionally, the size (smaller vs. larger) of the largest liver metastasis showed a trend towards significance (p=0.063). Based on the variables significantly associated with long-term survival, we developed a decision tree to facilitate clinical decision-making. The findings of this study demonstrate the existence of a bimodal survival pattern in patients with metastatic uveal melanoma. The presence of certain clinical characteristics at diagnosis of distant disease is associated with long-term survival. A decision tree was developed to facilitate clinical decision-making and to counsel patients about the expected course of disease.

Test Reviews: Euler, B. L. (2007). "Emotional Disturbance Decision Tree". Lutz, FL: Psychological Assessment Resources

ERIC Educational Resources Information Center

Tansy, Michael

2009-01-01

The Emotional Disturbance Decision Tree (EDDT) is a teacher-completed norm-referenced rating scale published by Psychological Assessment Resources, Inc., in Lutz, Florida. The 156-item EDDT was developed for use as part of a broader assessment process to screen and assist in the identification of 5- to 18-year-old children for the special…

Phytotechnology Technical and Regulatory Guidance Document

DTIC Science & Technology

2001-04-01

contaminated media is rather new. Throughout the development process of this document, we referred to the science as “ phytoremediation .” Recently...the media containing contaminants, we now refer to “phytotechnologies” as the overarching terminology, while using “ phytoremediation ” more...publication of the ITRC document, Phytoremediation Decision Tree. The decision tree was designed to allow potential users to take basic information

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Machine Learning Through Signature Trees. Applications to Human Speech.

ERIC Educational Resources Information Center

White, George M.

A signature tree is a binary decision tree used to classify unknown patterns. An attempt was made to develop a computer program for manipulating signature trees as a general research tool for exploring machine learning and pattern recognition. The program was applied to the problem of speech recognition to test its effectiveness for a specific…

Modeling individual tree survial

Treesearch

Quang V. Cao

2016-01-01

Information provided by growth and yield models is the basis for forest managers to make decisions on how to manage their forests. Among different types of growth models, whole-stand models offer predictions at stand level, whereas individual-tree models give detailed information at tree level. The well-known logistic regression is commonly used to predict tree...

Predicting Tillage Patterns in the Tiffin River Watershed Using Remote Sensing Methods

NASA Astrophysics Data System (ADS)

Brooks, C.; McCarty, J. L.; Dean, D. B.; Mann, B. F.

2012-12-01

Previous research in tillage mapping has focused primarily on utilizing low to no-cost, moderate (30 m to 15 m) resolution satellite data. Successful data processing techniques published in the scientific literature have focused on extracting and/or classifying tillage patterns through manipulation of spectral bands. For instance, Daughtry et al. (2005) evaluated several spectral indices for crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. A weak to moderate relationship between Landsat Thematic Mapper (TM) indices and crop residue cover was found; similar results were reported in Minnesota. Building on the findings from the scientific literature and previous work done by MTRI in the heavily agricultural Tiffin watershed of northwest Ohio and southeast Michigan, a decision tree classifier approach (also referred to as a classification tree) was used, linking several satellite data to on-the-ground tillage information in order to boost classification results. This approach included five tillage indices and derived products. A decision tree methodology enabled the development of statistically optimized (i.e., minimizing misclassification rates) classification algorithms at various desired time steps: monthly, seasonally, and annual over the 2006-2010 time period. Due to their flexibility, processing speed, and availability within all major remote sensing and statistical software packages, decision trees can ingest several data inputs from multiple sensors and satellite products, selecting only the bands, band ratios, indices, and products that further reduce misclassification errors. The project team created crop-specific tillage pattern classification trees whereby a training data set (~ 50% of available ground data) was created for production of the actual decision tree and a validation data set was set aside (~ 50% of available ground data) in order to assess the accuracy of the classification. A seasonal time step was used, optimizing a decision tree based on seasonal ground data for tillage patterns and satellite data and products for years 2006 through 2010. Annual crop type maps derived by the project team and the USDA Cropland Data Layer project was used an input to understand locations of corn, soybeans, wheat, etc. on a yearly basis. As previously stated, the robustness of the decision tree approach is the ability to implement various satellite data and products across temporal, spectral, and spatial resolutions, thereby improving the resulting classification and providing a reliable method that is not sensor-dependent. Tillage pattern classification from satellite imagery is not a simple task and has proven a challenge to previous researchers investigating this remote sensing topic. The team's decision tree method produced a practical, usable output within a focused project time period. Daughtry, C.S.T., Hunt Jr., E.R., Doraiswamy, P.C., McMurtrey III, J.E. 2005. Remote sensing the spatial distribution of crop residues. Agron. J. 97, 864-871.

Using decision tree models to depict primary care physicians CRC screening decision heuristics.

PubMed

Wackerbarth, Sarah B; Tarasenko, Yelena N; Curtis, Laurel A; Joyce, Jennifer M; Haist, Steven A

2007-10-01

The purpose of this study was to identify decision heuristics utilized by primary care physicians in formulating colorectal cancer screening recommendations. Qualitative research using in-depth semi-structured interviews. We interviewed 66 primary care internists and family physicians evenly drawn from academic and community practices. A majority of physicians were male, and almost all were white, non-Hispanic. Three researchers independently reviewed each transcript to determine the physician's decision criteria and developed decision trees. Final trees were developed by consensus. The constant comparative methodology was used to define the categories. Physicians were found to use 1 of 4 heuristics ("age 50," "age 50, if family history, then earlier," "age 50, if family history, then screen at age 40," or "age 50, if family history, then adjust relative to reference case") for the timing recommendation and 5 heuristics ["fecal occult blood test" (FOBT), "colonoscopy," "if not colonoscopy, then...," "FOBT and another test," and "a choice between options"] for the type decision. No connection was found between timing and screening type heuristics. We found evidence of heuristic use. Further research is needed to determine the potential impact on quality of care.

Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial LiDAR Point Cloud and Digital Imagery Datasets

NASA Astrophysics Data System (ADS)

Dogon-Yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-09-01

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

Decision-Tree Program

NASA Technical Reports Server (NTRS)

Buntine, Wray

1994-01-01

IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

Interpretable Categorization of Heterogeneous Time Series Data

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder.

PubMed

Fang, H; Lu, B; Wang, X; Zheng, L; Sun, K; Cai, W

2017-08-17

This study proposed a decision tree model to screen upper urinary tract damage (UUTD) for patients with neurogenic bladder (NGB). Thirty-four NGB patients with UUTD were recruited in the case group, while 78 without UUTD were included in the control group. A decision tree method, classification and regression tree (CART), was then applied to develop the model in which UUTD was used as a dependent variable and history of urinary tract infections, bladder management, conservative treatment, and urodynamic findings were used as independent variables. The urethra function factor was found to be the primary screening information of patients and treated as the root node of the tree; Pabd max (maximum abdominal pressure, >14 cmH2O), Pves max (maximum intravesical pressure, ≤89 cmH2O), and gender (female) were also variables associated with UUTD. The accuracy of the proposed model was 84.8%, and the area under curve was 0.901 (95%CI=0.844-0.958), suggesting that the decision tree model might provide a new and convenient way to screen UUTD for NGB patients in both undeveloped and developing areas.

Graphic Representations as Tools for Decision Making.

ERIC Educational Resources Information Center

Howard, Judith

2001-01-01

Focuses on the use of graphic representations to enable students to improve their decision making skills in the social studies. Explores three visual aids used in assisting students with decision making: (1) the force field; (2) the decision tree; and (3) the decision making grid. (CMK)

Alternative hot spot formation techniques using liquid deuterium-tritium layer inertial confinement fusion capsules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olson, R. E.; Leeper, R. J.

2013-09-27

The baseline DT ice layer inertial confinement fusion (ICF) ignition capsule design requires a hot spot convergence ratio of ~34 with a hot spot that is formed from DT mass originally residing in a very thin layer at the inner DT ice surface. In the present paper, we propose alternative ICF capsule designs in which the hot spot is formed mostly or entirely from mass originating within a spherical volume of DT vapor. Simulations of the implosion and hot spot formation in two DT liquid layer ICF capsule concepts—the DT wetted hydrocarbon (CH) foam concept and the “fast formed liquid”more » (FFL) concept—are described and compared to simulations of standard DT ice layer capsules. 1D simulations are used to compare the drive requirements, the optimal shock timing, the radial dependence of hot spot specific energy gain, and the hot spot convergence ratio in low vapor pressure (DT ice) and high vapor pressure (DT liquid) capsules. 2D simulations are used to compare the relative sensitivities to low-mode x-ray flux asymmetries in the DT ice and DT liquid capsules. It is found that the overall thermonuclear yields predicted for DT liquid layer capsules are less than yields predicted for DT ice layer capsules in simulations using comparable capsule size and absorbed energy. However, the wetted foam and FFL designs allow for flexibility in hot spot convergence ratio through the adjustment of the initial cryogenic capsule temperature and, hence, DT vapor density, with a potentially improved robustness to low-mode x-ray flux asymmetry.« less

Alternative hot spot formation techniques using liquid deuterium-tritium layer inertial confinement fusion capsules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olson, R. E.; Leeper, R. J.

2013-09-15

The baseline DT ice layer inertial confinement fusion (ICF) ignition capsule design requires a hot spot convergence ratio of ∼34 with a hot spot that is formed from DT mass originally residing in a very thin layer at the inner DT ice surface. In the present paper, we propose alternative ICF capsule designs in which the hot spot is formed mostly or entirely from mass originating within a spherical volume of DT vapor. Simulations of the implosion and hot spot formation in two DT liquid layer ICF capsule concepts—the DT wetted hydrocarbon (CH) foam concept and the “fast formed liquid”more » (FFL) concept—are described and compared to simulations of standard DT ice layer capsules. 1D simulations are used to compare the drive requirements, the optimal shock timing, the radial dependence of hot spot specific energy gain, and the hot spot convergence ratio in low vapor pressure (DT ice) and high vapor pressure (DT liquid) capsules. 2D simulations are used to compare the relative sensitivities to low-mode x-ray flux asymmetries in the DT ice and DT liquid capsules. It is found that the overall thermonuclear yields predicted for DT liquid layer capsules are less than yields predicted for DT ice layer capsules in simulations using comparable capsule size and absorbed energy. However, the wetted foam and FFL designs allow for flexibility in hot spot convergence ratio through the adjustment of the initial cryogenic capsule temperature and, hence, DT vapor density, with a potentially improved robustness to low-mode x-ray flux asymmetry.« less

The Effect of Defense R&D Expenditures on Military Capability and Technological Spillover

DTIC Science & Technology

2013-03-01

ix List of Figures Page Figure 1. Decision Tree for Sectoring R&D Units...approach, often called sectoring , categorizes R&D activities by funding source, and the functional approach categorizes R&D activities by their objective...economic objectives (defense, and control and care of environment) (OECD, 2002). Figure 1 shows the decision tree for sectoring R&D units and

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

NASA Astrophysics Data System (ADS)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters. Overall, the probability of detection of an event with a return period of 10 years is 62%. 44% of all 10-year flood peaks can be detected with a timing error of 2 hours or less. These results indicate that the modeling system can provide useful information about the timing and magnitude of flood events at ungauged sites.

Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model.

PubMed

Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

2018-02-09

Precise renal histopathological diagnosis will guide therapy strategy in patients with lupus nephritis. Blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) has been applicable noninvasive technique in renal disease. This current study was performed to explore whether BOLD MRI could contribute to diagnose renal pathological pattern. Adult patients with lupus nephritis renal pathological diagnosis were recruited for this study. Renal biopsy tissues were assessed based on the lupus nephritis ISN/RPS 2003 classification. The Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) was used to obtain functional magnetic resonance parameter, R2* values. Several functions of R2* values were calculated and used to construct algorithmic models for renal pathological patterns. In addition, the algorithmic models were compared as to their diagnostic capability. Both Histopathology and BOLD MRI were used to examine a total of twelve patients. Renal pathological patterns included five classes III (including 3 as class III + V) and seven classes IV (including 4 as class IV + V). Three algorithmic models, including decision tree, line discriminant, and logistic regression, were constructed to distinguish the renal pathological pattern of class III and class IV. The sensitivity of the decision tree model was better than that of the line discriminant model (71.87% vs 59.48%, P < 0.001) and inferior to that of the Logistic regression model (71.87% vs 78.71%, P < 0.001). The specificity of decision tree model was equivalent to that of the line discriminant model (63.87% vs 63.73%, P = 0.939) and higher than that of the logistic regression model (63.87% vs 38.0%, P < 0.001). The Area under the ROC curve (AUROCC) of the decision tree model was greater than that of the line discriminant model (0.765 vs 0.629, P < 0.001) and logistic regression model (0.765 vs 0.662, P < 0.001). BOLD MRI is a useful non-invasive imaging technique for the evaluation of lupus nephritis. Decision tree models constructed using functions of R2* values may facilitate the prediction of renal pathological patterns.

A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum β-Lactamase-Producing Organism.

PubMed

Goodman, Katherine E; Lessler, Justin; Cosgrove, Sara E; Harris, Anthony D; Lautenbach, Ebbing; Han, Jennifer H; Milstone, Aaron M; Massey, Colin J; Tamma, Pranita D

2016-10-01

Timely identification of extended-spectrum β-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy. We included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics. A total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively. Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

What's D&T For? Gathering and Comparing the Values of Design and Technology Academics and Trainee Teachers

ERIC Educational Resources Information Center

Hardy, Alison

2015-01-01

Some who read and research about Design & Technology (D&T) would say that the concept of value is key to understanding and defining D&T. Closer inspection reveals though that there are two ways in which values are defined in D&T: how values are taught and learnt about in D&T to use them to make judgments in D&T lessons, and…

Isoguanine quartets formed by d(T4isoG4T4): tetraplex identification and stability.

PubMed Central

Seela, F; Wei, C; Melenewski, A

1996-01-01

The self-aggregation of the oligonucleotide d(T4isoG4T4) (1) is investigated. Based on ion exchange HPLC experiments and CD spectroscopy, a tetrameric structure is identified. This structure was formed in the presence of sodium ions and shows almost the same chromatographic mobility on ion exchange HPLC as d(T4G4T4) (2). The ratio of aggregate versus monomer is temperature dependent and the tetraplex of [d(T4isoG4T4)]4 is more stable than that of [d(T4G4T4)]4. A mixture of d(T4isoG4T4) and d(T4G4T4) forms mixed tetraplexes containing strands of d(T4isoG4T4) and d(T4G4T4). PMID:9016664

Distillation Time as Tool for Improved Antimalarial Activity and Differential Oil Composition of Cumin Seed Oil.

PubMed

Zheljazkov, Valtcho D; Gawde, Archana; Cantrell, Charles L; Astatkie, Tess; Schlegel, Vicki

2015-01-01

A steam distillation extraction kinetics experiment was conducted to estimate essential oil yield, composition, antimalarial, and antioxidant capacity of cumin (Cuminum cyminum L.) seed (fruits). Furthermore, regression models were developed to predict essential oil yield and composition for a given duration of the steam distillation time (DT). Ten DT durations were tested in this study: 5, 7.5, 15, 30, 60, 120, 240, 360, 480, and 600 min. Oil yields increased with an increase in the DT. Maximum oil yield (content, 2.3 g/100 seed), was achieved at 480 min; longer DT did not increase oil yields. The concentrations of the major oil constituents α-pinene (0.14-0.5% concentration range), β-pinene (3.7-10.3% range), γ-cymene (5-7.3% range), γ-terpinene (1.8-7.2% range), cumin aldehyde (50-66% range), α-terpinen-7-al (3.8-16% range), and β-terpinen-7-al (12-20% range) varied as a function of the DT. The concentrations of α-pinene, β-pinene, γ-cymene, γ-terpinene in the oil increased with the increase of the duration of the DT; α-pinene was highest in the oil obtained at 600 min DT, β-pinene and γ-terpinene reached maximum concentrations in the oil at 360 min DT; γ-cymene reached a maximum in the oil at 60 min DT, cumin aldehyde was high in the oils obtained at 5-60 min DT, and low in the oils obtained at 240-600 min DT, α-terpinen-7-al reached maximum in the oils obtained at 480 or 600 min DT, whereas β-terpinen-7-al reached a maximum concentration in the oil at 60 min DT. The yield of individual oil constituents (calculated from the oil yields and the concentration of a given compound at a particular DT) increased and reached a maximum at 480 or 600 min DT. The antimalarial activity of the cumin seed oil obtained during the 0-5 and at 5-7.5 min DT timeframes was twice higher than the antimalarial activity of the oils obtained at the other DT. This study opens the possibility for distinct marketing and utilization for these improved oils. The antioxidant capacity of the oil was highest in the oil obtained at 30 min DT and lowest in the oil from 360 min DT. The Michaelis-Menton and the Power nonlinear regression models developed in this study can be utilized to predict essential oil yield and composition of cumin seed at any given duration of DT and may also be useful to compare previous reports on cumin oil yield and composition. DT can be utilized to obtain cumin seed oil with improved antimalarial activity, improved antioxidant capacity, and with various compositions.

Ensemble stump classifiers and gene expression signatures in lung cancer.

PubMed

Frey, Lewis; Edgerton, Mary; Fisher, Douglas; Levy, Shawn

2007-01-01

Microarray data sets for cancer tumor tissue generally have very few samples, each sample having thousands of probes (i.e., continuous variables). The sparsity of samples makes it difficult for machine learning techniques to discover probes relevant to the classification of tumor tissue. By combining data from different platforms (i.e., data sources), data sparsity is reduced, but this typically requires normalizing data from the different platforms, which can be non-trivial. This paper proposes a variant on the idea of ensemble learners to circumvent the need for normalization. To facilitate comprehension we build ensembles of very simple classifiers known as decision stumps--decision trees of one test each. The Ensemble Stump Classifier (ESC) identifies an mRNA signature having three probes and high accuracy for distinguishing between adenocarcinoma and squamous cell carcinoma of the lung across four data sets. In terms of accuracy, ESC outperforms a decision tree classifier on all four data sets, outperforms ensemble decision trees on three data sets, and simple stump classifiers on two data sets.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

PubMed

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis.

PubMed

Dias, Cláudia Camila; Pereira Rodrigues, Pedro; Fernandes, Samuel; Portela, Francisco; Ministro, Paula; Martins, Diana; Sousa, Paula; Lago, Paula; Rosa, Isadora; Correia, Luis; Moura Santos, Paula; Magro, Fernando

2017-01-01

Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

NASA Astrophysics Data System (ADS)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-01

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Trees Are Terrific!

ERIC Educational Resources Information Center

Braus, Judy, Ed.

1992-01-01

Ranger Rick's NatureScope is a creative education series dedicated to inspiring in children an understanding and appreciation of the natural world while developing the skills they will need to make responsible decisions about the environment. Contents are organized into the following sections: (1) "What Makes a Tree a Tree?," including…

Immunogenicity of diphtheria toxoid and poly(I:C) loaded cationic liposomes after hollow microneedle-mediated intradermal injection in mice.

PubMed

Du, Guangsheng; Leone, Mara; Romeijn, Stefan; Kersten, Gideon; Jiskoot, Wim; Bouwstra, Joke A

2018-06-02

In this study, we aimed to investigate the immunogenicity of cationic liposomes loaded with diphtheria toxoid (DT) and poly(I:C) after hollow microneedle-mediated intradermal vaccination in mice. The following liposomal formulations were studied: DT loaded liposomes, a mixture of free DT and poly(I:C)-loaded liposomes, a mixture of DT-loaded liposomes and free poly(I:C), and liposomal formulations with DT and poly(I:C) either individually or co-encapsulated in the liposomes. Reference groups were DT solution adjuvanted with or without poly(I:C) (DT/poly(I:C)). The liposomal formulations were characterized in terms of particle size, zeta potential, loading and release of DT and poly(I:C). After intradermal injection of BALB/c mice with the formulations through a hollow microneedle, the immunogenicity was assessed by DT-specific ELISAs. All formulations induced similar total IgG and IgG1 titers. However, all the liposomal groups containing both DT and poly(I:C) showed enhanced IgG2a titers compared to DT/poly(I:C) solution, indicating that the immune response was skewed towards a Th1 direction. This enhancement was similar for all liposomal groups that contain both DT and poly(I:C) in the formulations. Our results reveal that a mixture of DT encapsulated liposomes and poly(I:C) encapsulated liposomes have a similar effect on the antibody responses as DT and poly(I:C) co-encapsulated liposomes. These findings may have implications for future design of liposomal vaccine delivery systems. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

PubMed

Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu

2018-06-15

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

NASA Technical Reports Server (NTRS)

Otterstatter, Matthew R.

2005-01-01

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

The use of decision trees and naïve Bayes algorithms and trace element patterns for controlling the authenticity of free-range-pastured hens' eggs.

PubMed

Barbosa, Rommel Melgaço; Nacano, Letícia Ramos; Freitas, Rodolfo; Batista, Bruno Lemos; Barbosa, Fernando

2014-09-01

This article aims to evaluate 2 machine learning algorithms, decision trees and naïve Bayes (NB), for egg classification (free-range eggs compared with battery eggs). The database used for the study consisted of 15 chemical elements (As, Ba, Cd, Co, Cs, Cu, Fe, Mg, Mn, Mo, Pb, Se, Sr, V, and Zn) determined in 52 eggs samples (20 free-range and 32 battery eggs) by inductively coupled plasma mass spectrometry. Our results demonstrated that decision trees and NB associated with the mineral contents of eggs provide a high level of accuracy (above 80% and 90%, respectively) for classification between free-range and battery eggs and can be used as an alternative method for adulteration evaluation. © 2014 Institute of Food Technologists®

Optimization of hepatobiliary phase delay time of Gd-EOB-DTPA-enhanced magnetic resonance imaging for identification of hepatocellular carcinoma in patients with cirrhosis of different degrees of severity.

PubMed

Wu, Jian-Wei; Yu, Yue-Cheng; Qu, Xian-Li; Zhang, Yan; Gao, Hong

2018-01-21

To optimize the hepatobiliary phase delay time (HBP-DT) of Gd-EOB-DTPA-enhanced magnetic resonance imaging (GED-MRI) for more efficient identification of hepatocellular carcinoma (HCC) occurring in different degrees of cirrhosis assessed by Child-Pugh (CP) score. The liver parenchyma signal intensity (LPSI), the liver parenchyma (LP)/HCC signal ratios, and the visibility of HCC at HBP-DT of 5, 10, 15, 20, and 25 min ( i.e ., DT-5, DT-10, DT-15, DT-20, and DT-25 ) after injection of Gd-EOB-DTPA were collected and analyzed in 73 patients with cirrhosis of different degrees of severity (including 42 patients suffering from HCC) and 18 healthy adult controls. The LPSI increased with HBP-DT more significantly in the healthy group than in the cirrhosis group ( F = 17.361, P < 0.001). The LP/HCC signal ratios had a significant difference ( F = 12.453, P < 0.001) among various HBP-DT points, as well as between CP-A and CP-B/C subgroups ( F = 9.761, P < 0.001). The constituent ratios of HCC foci identified as obvious hypointensity (+++), moderate hypointensity (++), and mild hypointensity or isointensity (+/-) kept stable from DT-10 to DT-25: 90.6%, 9.4%, and 0.0% in the CP-A subgroup; 50.0%, 50.0%, and 0.0% in the CP-B subgroup; and 0.0%, 0.0%, and 100.0% in the CP-C subgroup, respectively. The severity of liver cirrhosis has significant negative influence on the HCC visualization by GED-MRI. DT-10 is more efficient and practical than other HBP-DT points to identify most of HCC foci emerging in CP-A cirrhosis, as well as in CP-B cirrhosis; but an HBP-DT of 15 min or longer seems more appropriate than DT-10 for visualization of HCC in patients with CP-C cirrhosis.

Pollution mitigation and carbon sequestration by an urban forest.

PubMed

Brack, C L

2002-01-01

At the beginning of the 1900s, the Canberra plain was largely treeless. Graziers had carried out extensive clearing of the original trees since the 1820s leaving only scattered remnants and some plantings near homesteads. With the selection of Canberra as the site for the new capital of Australia, extensive tree plantings began in 1911. These trees have delivered a number of benefits, including aesthetic values and the amelioration of climatic extremes. Recently, however, it was considered that the benefits might extend to pollution mitigation and the sequestration of carbon. This paper outlines a case study of the value of the Canberra urban forest with particular reference to pollution mitigation. This study uses a tree inventory, modelling and decision support system developed to collect and use data about trees for tree asset management. The decision support system (DISMUT) was developed to assist in the management of about 400,000 trees planted in Canberra. The size of trees during the 5-year Kyoto Commitment Period was estimated using DISMUT and multiplied by estimates of value per square meter of canopy derived from available literature. The planted trees are estimated to have a combined energy reduction, pollution mitigation and carbon sequestration value of US$20-67 million during the period 2008-2012.

Using real options analysis to support strategic management decisions

NASA Astrophysics Data System (ADS)

Kabaivanov, Stanimir; Markovska, Veneta; Milev, Mariyan

2013-12-01

Decision making is a complex process that requires taking into consideration multiple heterogeneous sources of uncertainty. Standard valuation and financial analysis techniques often fail to properly account for all these sources of risk as well as for all sources of additional flexibility. In this paper we explore applications of a modified binomial tree method for real options analysis (ROA) in an effort to improve decision making process. Usual cases of use of real options are analyzed with elaborate study on the applications and advantages that company management can derive from their application. A numeric results based on extending simple binomial tree approach for multiple sources of uncertainty are provided to demonstrate the improvement effects on management decisions.

Inertial Navigation System Aiding Using Vision

DTIC Science & Technology

2013-03-01

abp a + Cba d dt ( pa ) + d dt ( rbba ) (2.11) vb = d dt ( rbba ) + Cba (Ω a... abp a + va) (2.12) where ddt (r b ba) accounts for the relative velocity betwwen the a-frame and b-frame, CbaΩaabp a is the instantaneous velocity of p...frame. Taking another time derivative of Eq. 2.12 results in: d dt ( vb ) , ab = d2 dt2 rbba + d dt [ Cba (Ω a abp a + va) ] (2.13) = r̈bba + dCba

Improving ensemble decision tree performance using Adaboost and Bagging

NASA Astrophysics Data System (ADS)

Hasan, Md. Rajib; Siraj, Fadzilah; Sainin, Mohd Shamrie

2015-12-01

Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers. However, in a ensemble settings the performance depends on the selection of suitable base classifier. This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted. The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.

Knowledge Quality Functions for Rule Discovery

DTIC Science & Technology

1994-09-01

Managers in many organizations finding themselves in the possession of large and rapidly growing databases are beginning to suspect the information in their...missing values (Smyth and Goodman, 1992, p. 303). Decision trees "tend to grow very large for realistic applications and are thus difficult to interpret...by humans" (Holsheimer, 1994, p. 42). Decision trees also grow excessively complicated in the presence of noisy databases (Dhar and Tuzhilin, 1993, p

Structural Equation Model Trees

ERIC Educational Resources Information Center

Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

2013-01-01

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

PubMed

Esmaily, Habibollah; Tayefi, Maryam; Doosti, Hassan; Ghayour-Mobarhan, Majid; Nezami, Hossein; Amirabadizadeh, Alireza

2018-04-24

We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. A cross-sectional study. The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

Distillation Time as Tool for Improved Antimalarial Activity and Differential Oil Composition of Cumin Seed Oil

PubMed Central

Zheljazkov, Valtcho D.; Gawde, Archana; Cantrell, Charles L.; Astatkie, Tess; Schlegel, Vicki

2015-01-01

A steam distillation extraction kinetics experiment was conducted to estimate essential oil yield, composition, antimalarial, and antioxidant capacity of cumin (Cuminum cyminum L.) seed (fruits). Furthermore, regression models were developed to predict essential oil yield and composition for a given duration of the steam distillation time (DT). Ten DT durations were tested in this study: 5, 7.5, 15, 30, 60, 120, 240, 360, 480, and 600 min. Oil yields increased with an increase in the DT. Maximum oil yield (content, 2.3 g/100 seed), was achieved at 480 min; longer DT did not increase oil yields. The concentrations of the major oil constituents α-pinene (0.14–0.5% concentration range), β-pinene (3.7–10.3% range), γ-cymene (5–7.3% range), γ-terpinene (1.8–7.2% range), cumin aldehyde (50–66% range), α-terpinen-7-al (3.8–16% range), and β-terpinen-7-al (12–20% range) varied as a function of the DT. The concentrations of α-pinene, β-pinene, γ-cymene, γ-terpinene in the oil increased with the increase of the duration of the DT; α-pinene was highest in the oil obtained at 600 min DT, β-pinene and γ-terpinene reached maximum concentrations in the oil at 360 min DT; γ-cymene reached a maximum in the oil at 60 min DT, cumin aldehyde was high in the oils obtained at 5–60 min DT, and low in the oils obtained at 240–600 min DT, α-terpinen-7-al reached maximum in the oils obtained at 480 or 600 min DT, whereas β-terpinen-7-al reached a maximum concentration in the oil at 60 min DT. The yield of individual oil constituents (calculated from the oil yields and the concentration of a given compound at a particular DT) increased and reached a maximum at 480 or 600 min DT. The antimalarial activity of the cumin seed oil obtained during the 0–5 and at 5–7.5 min DT timeframes was twice higher than the antimalarial activity of the oils obtained at the other DT. This study opens the possibility for distinct marketing and utilization for these improved oils. The antioxidant capacity of the oil was highest in the oil obtained at 30 min DT and lowest in the oil from 360 min DT. The Michaelis-Menton and the Power nonlinear regression models developed in this study can be utilized to predict essential oil yield and composition of cumin seed at any given duration of DT and may also be useful to compare previous reports on cumin oil yield and composition. DT can be utilized to obtain cumin seed oil with improved antimalarial activity, improved antioxidant capacity, and with various compositions. PMID:26641276

Gait and Cognition in Parkinson's Disease: Cognitive Impairment Is Inadequately Reflected by Gait Performance during Dual Task.

PubMed

Gaßner, Heiko; Marxreiter, Franz; Steib, Simon; Kohl, Zacharias; Schlachetzki, Johannes C M; Adler, Werner; Eskofier, Bjoern M; Pfeifer, Klaus; Winkler, Jürgen; Klucken, Jochen

2017-01-01

Cognitive and gait deficits are common symptoms in Parkinson's disease (PD). Motor-cognitive dual tasks (DTs) are used to explore the interplay between gait and cognition. However, it is unclear if DT gait performance is indicative for cognitive impairment. Therefore, the aim of this study was to investigate if cognitive deficits are reflected by DT costs of spatiotemporal gait parameters. Cognitive function, single task (ST) and DT gait performance were investigated in 67 PD patients. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) followed by a standardized, sensor-based gait test and the identical gait test while subtracting serial 3's. Cognitive impairment was defined by a MoCA score <26. DT costs in gait parameters [(DT - ST)/ST × 100] were calculated as a measure of DT effect on gait. Correlation analysis was used to evaluate the association between MoCA performance and gait parameters. In a linear regression model, DT gait costs and clinical confounders (age, gender, disease duration, motor impairment, medication, and depression) were correlated to cognitive performance. In a subgroup analysis, we compared matched groups of cognitively impaired and unimpaired PD patients regarding differences in ST, DT, and DT gait costs. Correlation analysis revealed weak correlations between MoCA score and DT costs of gait parameters ( r / r Sp  ≤ 0.3). DT costs of stride length, swing time variability, and maximum toe clearance (| r / r Sp | > 0.2) were included in a regression analysis. The parameters only explain 8% of the cognitive variance. In combination with clinical confounders, regression analysis showed that these gait parameters explained 30% of MoCA performance. Group comparison revealed strong DT effects within both groups (large effect sizes), but significant between-group effects in DT gait costs were not observed. These findings suggest that DT gait performance is not indicative for cognitive impairment in PD. DT effects on gait parameters were substantial in cognitively impaired and unimpaired patients, thereby potentially overlaying the effect of cognitive impairment on DT gait costs. Limits of the MoCA in detecting motor-function specific cognitive performance or variable individual response to the DT as influencing factors cannot be excluded. Therefore, DT gait parameters as marker for cognitive performance should be carefully interpreted in the clinical context.

Environmental justice and factors that influence participation in tree planting programs in Portland, Oregon, U.S

Treesearch

Geoffrey H. Donovan; John Mills

2014-01-01

Many cities have policies encouraging homeowners to plant trees. For these policies to be effective, it is important to understand what motivates a homeowner’s tree-planting decision. Researchers address this question by identifying variables that influence participation in a tree-planting program in Portland, Oregon, U.S. According to the study, homeowners with street...

Canadian Registry of ICD Implant Testing procedures (CREDIT): current practice, risks, and costs of intraoperative defibrillation testing.

PubMed

Healey, Jeff S; Dorian, Paul; Mitchell, L Brent; Talajic, Mario; Philippon, Francois; Simpson, Chris; Yee, Raymond; Morillo, Carlos A; Lamy, Andre; Basta, Magdy; Birnie, David H; Wang, Xiaoyin; Nair, Girish M; Crystal, Eugene; Kerr, Charles R; Connolly, Stuart J

2010-02-01

There is uncertainty about the proper role of defibrillation testing (DT) at the time of implantable cardioverter defibrillator (ICD) insertion. A prospective registry was conducted at 13 sites in Canada between January 2006 and October 2007. To document the details of DT, the reasons for not conducting DT, and the costs and complications associated with DT. DT was conducted at implantation in 230 of 361 patients (64%). DT was more likely to be conducted for new implants compared with impulse generator replacements (71% vs 32%, P = 0.0001), but was similar for primary and secondary prevention indications (64% vs 63%, P = NS). Among patients not having DT, the reason(s) given were: considered unnecessary (44%); considered unsafe, mainly due to persistent atrial fibrillation (37%); lack of an anesthetist (20%); and, patient or physician preference (6%). When performed, DT consisted of a single successful shock > or = 10J below maximum device output in 65% of cases. A 10J safety-margin was met by 97% of patients, requiring system modification in 2.3%. Major perioperative complications occurred in 4.4% of patients having DT versus 6.6% of patients not having DT (P = NS). ICD insertion was $844 more expensive for patients having DT (P = 0.16), largely due to increased costs ($28,017 vs $24,545) among patients having impulse generator replacement (P = 0.02). DT was not performed in a third of ICD implants, usually due to a perceived lack of need or relative contraindication.

Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging.

PubMed

Yang, Cheng-Hong; Wu, Kuo-Chuan; Chuang, Li-Yeh; Chang, Hsueh-Wei

2018-01-01

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a r ibulose diphosphate carboxylase ( rbcL ) S NP b arcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

Kernel and divergence techniques in high energy physics separations

NASA Astrophysics Data System (ADS)

Bouř, Petr; Kůs, Václav; Franc, Jiří

2017-10-01

Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.

Max dD/Dt: A Novel Parameter to Assess Fetal Cardiac Contractility and a Substitute for Max dP/Dt.

PubMed

Fujita, Yasuyuki; Kiyokoba, Ryo; Yumoto, Yasuo; Kato, Kiyoko

2018-07-01

Aortic pulse waveforms are composed of a forward wave from the heart and a reflection wave from the periphery. We focused on this forward wave and suggested a new parameter, the maximum slope of aortic pulse waveforms (max dD/dt), for fetal cardiac contractility. Max dD/dt was calculated from fetal aortic pulse waveforms recorded with an echo-tracking system. A normal range of max dD/dt was constructed in 105 healthy fetuses using linear regression analysis. Twenty-two fetuses with suspected fetal cardiac dysfunction were divided into normal and decreased max dD/dt groups, and their clinical parameters were compared. Max dD/dt of aortic pulse waveforms increased linearly with advancing gestational age (r = 0.93). The decreased max dD/dt was associated with abnormal cardiotocography findings and short- and long-term prognosis. In conclusion, max dD/dt calculated from the aortic pulse waveforms in fetuses can substitute for max dP/dt, an index of cardiac contractility in adults. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Predicting creativity: the role of psychometric schizotypy and cannabis use in divergent thinking.

PubMed

Minor, Kyle S; Firmin, Ruth L; Bonfils, Kelsey A; Chun, Charlotte A; Buckner, Julia D; Cohen, Alex S

2014-12-15

Evidence suggests that divergent thinking (DT), a measure of creativity, is associated with positive schizotypy and cannabis use. Given the high rates of cannabis use among those with schizotypy, it is unclear if the relation of DT to schizotypy is due to co-occurring cannabis use. In this study, we examined the relations between DT, schizotypy, and cannabis use among positive schizotypy (n=66), negative schizotypy (n=22), and non-schizotypy (n=60) groups. Results revealed that DT was greater in the positive schizotypy group, on the order of small to medium effects, compared to negative and non-schizotypy groups. Cannabis use and DT were associated in the non-schizotypy group, but not in the positive or negative schizotypy groups. Across all groups, positive schizotypy significantly predicted DT; however, cannabis use was not a significant predictor of DT. In line with previous findings, cannabis use and DT were only related in individuals low in creativity. This suggests that a ceiling effect may be present, with only cannabis users who are low in creativity receiving any increase in DT. Future research should aim to clarify the DT-cannabis relationship. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Regulation of BRCA1 Function by DNA Damage-Induced Site-Specific Phosphorylation

DTIC Science & Technology

2005-06-01

Nature 382, 678-679. 8. Haile , D.T., and Parvin, J.D. (1999). Activation of transcription in vitro by the BRCAI Carboxyl-terminal domain. J. Biol. Chem...for Public Release; Distribution Unlimited The views, opinions and/or findings contained in this report are those of the author(s) and should not be...construed as an official Department of the Army position, policy or decision unless so designated by other documentation. 20051101 125 REPOR DCI P Form

Proceedings of the Seminar on U.S.-Italian Armaments Cooperation Held in Washington, DC on 25-27 June 1979,

DTIC Science & Technology

1979-01-01

briefed several of the NATO allies including Italy, FRG, and Franch on the Copperhead system. Last year we conducted some testing of Copperhead...delivery in May 1981. The ini- tial production contract will be a sole-source type with Food Machinery Corporation. The competitive procurement procedures... Food Machinery Corporation. The first prototype vehicle was received on December 1, 1978, and after DT/OT testing, a production decision will be made in

Are overeating and food addiction related to distress tolerance? An examination of residents with obesity from a U.S. metropolitan area.

PubMed

Kozak, Andrea T; Davis, Jessica; Brown, Ryan; Grabowski, Matthew

Low distress tolerance (DT) is an inability to handle negative emotions. There is strong support for the connection between low DT and substance addiction, which suggests that the former might be related to food addiction (FA). Previous work found that low DT was related to overeating in a college sample. The current study had two primary aims: (1) to determine whether low DT is associated with overeating in a sample of participants with diverse races and incomes, and (2) to investigate the relationships among DT and body mass index (BMI) as well as DT and FA symptoms. DT as a moderator of the association between general overeating and FA was also explored. One hundred and ninety residents of Metropolitan Detroit communities (mean age: 41.71; 45.8% male; 34.7% non-White race; 47.4% with obesity) completed the DT Scale, Dutch Eating Behavior Questionnaire, Three Factor Eating Questionnaire, and Yale FA Scale. BMI was based on measured weight and height. After adjusting for covariates, linear regression models found significant negative relationships between DT and emotional eating (P<0.001), external eating (P<0.001), disinhibition (P<0.001), FA (P<0.001), and BMI (P<0.01). DT was determined to be a moderator, such that among individuals who endorsed high levels of overeating, those with low DT reported more FA symptoms than those with high DT. These findings suggest interventions targeting low DT should be considered to reduce overeating, which is a precursor and maintenance factor of obesity and FA. Copyright © 2016 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

A divide and conquer approach to cope with uncertainty, human health risk, and decision making in contaminant hydrology

NASA Astrophysics Data System (ADS)

de Barros, Felipe P. J.; Bolster, Diogo; Sanchez-Vila, Xavier; Nowak, Wolfgang

2011-05-01

Assessing health risk in hydrological systems is an interdisciplinary field. It relies on the expertise in the fields of hydrology and public health and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties and variabilities present in hydrological, physiological, and human behavioral parameters. Despite significant theoretical advancements in stochastic hydrology, there is still a dire need to further propagate these concepts to practical problems and to society in general. Following a recent line of work, we use fault trees to address the task of probabilistic risk analysis and to support related decision and management problems. Fault trees allow us to decompose the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural divide and conquer approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance, and stage of analysis. Three differences are highlighted in this paper when compared to previous works: (1) The fault tree proposed here accounts for the uncertainty in both hydrological and health components, (2) system failure within the fault tree is defined in terms of risk being above a threshold value, whereas previous studies that used fault trees used auxiliary events such as exceedance of critical concentration levels, and (3) we introduce a new form of stochastic fault tree that allows us to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting.

Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

PubMed

Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

2008-07-01

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

Comparison of post-contrast 3D-T1-MPRAGE, 3D-T1-SPACE and 3D-T2-FLAIR MR images in evaluation of meningeal abnormalities at 3-T MRI.

PubMed

Jeevanandham, Balaji; Kalyanpur, Tejas; Gupta, Prashant; Cherian, Mathew

2017-06-01

This study was to assess the usefulness of newer three-dimensional (3D)-T 1 sampling perfection with application optimized contrast using different flip-angle evolutions (SPACE) and 3D-T 2 fluid-attenuated inversion recovery (FLAIR) sequences in evaluation of meningeal abnormalities. 78 patients who presented with high suspicion of meningeal abnormalities were evaluated using post-contrast 3D-T 2 -FLAIR, 3D-T 1 magnetization-prepared rapid gradient-echo (MPRAGE) and 3D-T 1 -SPACE sequences. The images were evaluated independently by two radiologists for cortical gyral, sulcal space, basal cisterns and dural enhancement. The diagnoses were confirmed by further investigations including histopathology. Post-contrast 3D-T 1 -SPACE and 3D-T 2 -FLAIR images yielded significantly more information than MPRAGE images (p < 0.05 for both SPACE and FLAIR images) in detection of meningeal abnormalities. SPACE images best demonstrated abnormalities in dural and sulcal spaces, whereas FLAIR was useful for basal cisterns enhancement. Both SPACE and FLAIR performed equally well in detection of gyral enhancement. In all 10 patients, where both SPACE and T 2 -FLAIR images failed to demonstrate any abnormality, further analysis was also negative. The 3D-T 1 -SPACE sequence best demonstrated abnormalities in dural and sulcal spaces, whereas FLAIR was useful for abnormalities in basal cisterns. Both SPACE and FLAIR performed holds good for detection of gyral enhancement. Post-contrast SPACE and FLAIR sequences are superior to the MPRAGE sequence for evaluation of meningeal abnormalities and when used in combination have the maximum sensitivity for leptomeningeal abnormalities. The negative-predictive value is nearly 100%, where no leptomeningeal abnormality was detected on these sequences. Advances in knowledge: Post-contrast 3D-T 1 -SPACE and 3D-T 2 -FLAIR images are more useful than 3D-T 1 -MPRAGE images in evaluation of meningeal abnormalities.

Diastolic Function in Normal Sinus Rhythm vs. Chronic Atrial Fibrillation: Comparison by Fractionation of E-wave Deceleration Time into Stiffness and Relaxation Components.

PubMed

Mossahebi, Sina; Kovács, Sándor J

2014-01-01

Although the electrophysiologic derangement responsible for atrial fibrillation (AF) has been elucidated, how AF remodels the ventricular chamber and affects diastolic function (DF) has not been fully characterized. The previously validated Parametrized Diastolic Filling (PDF) formalism models suction-initiated filling kinematically and generates error-minimized fits to E-wave contours using unique load (x o ), relaxation (c), and stiffness (k) parameters. It predicts that E-wave deceleration time (DT) is a function of both stiffness and relaxation. Ascribing DT s to stiffness and DTr to relaxation such that DT=DT s +DT r is legitimate because of causality and their predicted and observed high correlation (r=0.82 and r=0.94) with simultaneous (diastatic) chamber stiffness (dP/dV) and isovolumic relaxation (tau), respectively. We analyzed simultaneous echocardiography-cardiac catheterization data and compared 16 age matched, chronic AF subjects to 16, normal sinus rhythm (NSR) subjects (650 beats). All subjects had diastatic intervals. Conventional DF parameters (DT, AT, E peak , E dur , E-VTI, E/E') and E-wave derived PDF parameters (c, k, DT s , DT r ) were compared. Total DT and DT s , DT r in AF were shorter than in NSR (p<0.005), chamber stiffness, (k) in AF was higher than in NSR (p<0.001). For NSR, 75% of DT was due to stiffness and 25% was due to relaxation whereas for AF 81% of DT was due to stiffness and 19% was due to relaxation (p<0.005). We conclude that compared to NSR, increased chamber stiffness is one measurable consequence of chamber remodeling in chronic, rate controlled AF. A larger fraction of E-wave DT in AF is due to stiffness compared to NSR. By trending individual subjects, this method can elucidate and characterize the beneficial or adverse long-term effects on chamber remodeling due to alternative therapies in terms of chamber stiffness and relaxation.

Enhancement of brain-targeting delivery of danshensu in rat through conjugation with pyrazine moiety to form danshensu-pyrazine ester.

PubMed

Hui, Ailing; Yin, Huayang; Zhang, Zheng; Zhou, An; Chen, Jingchao; Yang, Li; Wu, Zeyu; Zhang, Wencheng

2018-06-01

Tetramethylpyrazine was introduced to the structure of danshensu (DSS) as P-glycoprotein (P-gp)-inhibiting carrier, designing some novel brain-targeting DSS-pyrazine derivatives via prodrug delivery strategy. Following the virtual screening, three DSS-pyrazine esters (DT1, DT2, DT3) were selected because of their better prediction parameters related to brain-targeting. Among them, DT3 was thought to be a promising candidate due to its appropriate bioreversible property in vitro release assay. Further investigation with regard to DT3's brain-targeting effects in vivo was also reported in this study. High-performance liquid chromatography-diode array detection (HPLC-DAD) method was established for the quantitative determination of DT3 and DSS in rat plasma, brain homogenate after intravenous injection. In vivo metabolism of DT3 indicated that it was first converted into DT1, DT2, then the generation of DSS, which could be the result of carboxylesterase activity in rat blood and brain tissue. Moreover, the brain pharmacokinetics of DT3 was significantly altered with 2.16 times increase in half-life compared with that of DSS, and its drug targeting index (DTI) was up to 16.95. Above these data demonstrated that DT3 had better tendency of brain-targeting delivery, which would be positive for the treatment of brain-related disorders.

Abscisic acid (ABA) sensitivity regulates desiccation tolerance in germinated Arabidopsis seeds.

PubMed

Maia, Julio; Dekkers, Bas J W; Dolle, Miranda J; Ligterink, Wilco; Hilhorst, Henk W M

2014-07-01

During germination, orthodox seeds lose their desiccation tolerance (DT) and become sensitive to extreme drying. Yet, DT can be rescued, in a well-defined developmental window, by the application of a mild osmotic stress before dehydration. A role for abscisic acid (ABA) has been implicated in this stress response and in DT re-establishment. However, the path from the sensing of an osmotic cue and its signaling to DT re-establishment is still largely unknown. Analyses of DT, ABA sensitivity, ABA content and gene expression were performed in desiccation-sensitive (DS) and desiccation-tolerant Arabidopsis thaliana seeds. Furthermore, loss and re-establishment of DT in germinated Arabidopsis seeds was studied in ABA-deficient and ABA-insensitive mutants. We demonstrate that the developmental window in which DT can be re-established correlates strongly with the window in which ABA sensitivity is still present. Using ABA biosynthesis and signaling mutants, we show that this hormone plays a key role in DT re-establishment. Surprisingly, re-establishment of DT depends on the modulation of ABA sensitivity rather than enhanced ABA content. In addition, the evaluation of several ABA-insensitive mutants, which can still produce normal desiccation-tolerant seeds, but are impaired in the re-establishment of DT, shows that the acquisition of DT during seed development is genetically different from its re-establishment during germination. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

Construction accident narrative classification: An evaluation of text mining techniques.

PubMed

Goh, Yang Miang; Ubeynarayana, C U

2017-11-01

Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classify accident and near miss narratives will be very significant. This study aims to evaluate the utility of various text mining classification techniques in classifying 1000 publicly available construction accident narratives obtained from the US OSHA website. The study evaluated six machine learning algorithms, including support vector machine (SVM), linear regression (LR), random forest (RF), k-nearest neighbor (KNN), decision tree (DT) and Naive Bayes (NB), and found that SVM produced the best performance in classifying the test set of 251 cases. Further experimentation with tokenization of the processed text and non-linear SVM were also conducted. In addition, a grid search was conducted on the hyperparameters of the SVM models. It was found that the best performing classifiers were linear SVM with unigram tokenization and radial basis function (RBF) SVM with uni-gram tokenization. In view of its relative simplicity, the linear SVM is recommended. Across the 11 labels of accident causes or types, the precision of the linear SVM ranged from 0.5 to 1, recall ranged from 0.36 to 0.9 and F1 score was between 0.45 and 0.92. The reasons for misclassification were discussed and suggestions on ways to improve the performance were provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

PubMed Central

Garcia Lopez, Sebastian; Kim, Philip M.

2014-01-01

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Chattopadhyay, Subhagata; Yu, Wenwei; Ang, Peng Chuan Alvin

2011-06-01

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

Reliable structural information from multiscale decomposition with the Mellor-Brady filter

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael

2009-08-01

Image-based medical diagnosis typically relies on the (poorly reproducible) subjective classification of textures in order to differentiate between diseased and healthy pathology. Clinicians claim that significant benefits would arise from quantitative measures to inform clinical decision making. The first step in generating such measures is to extract local image descriptors - from noise corrupted and often spatially and temporally coarse resolution medical signals - that are invariant to illumination, translation, scale and rotation of the features. The Dual-Tree Complex Wavelet Transform (DT-CWT) provides a wavelet multiresolution analysis (WMRA) tool e.g. in 2D with good properties, but has limited rotational selectivity. Also, it requires computationally-intensive steering due to the inherently 1D operations performed. The monogenic signal, which is defined in n >= 2D with the Riesz transform gives excellent orientation information without the need for steering. Recent work has suggested the Monogenic Riesz-Laplace wavelet transform as a possible tool for integrating these two concepts into a coherent mathematical framework. We have found that the proposed construction suffers from a lack of rotational invariance and is not optimal for retrieving local image descriptors. In this paper we show: 1. Local frequency and local phase from the monogenic signal are not equivalent, especially in the phase congruency model of a "feature", and so they are not interchangeable for medical image applications. 2. The accuracy of local phase computation may be improved by estimating the denoising parameters while maximizing a new measure of "featureness".

Re-Construction of Reference Population and Generating Weights by Decision Tree

DTIC Science & Technology

2017-07-21

2017 Claflin University Orangeburg, SC 29115 DEFENSE EQUAL OPPORTUNITY MANAGEMENT INSTITUTE RESEARCH, DEVELOPMENT, AND STRATEGIC...Original Dataset 32 List of Figures in Appendix B Figure 1: Flow and Components of Project 20 Figure 2: Decision Tree 31 Figure 3: Effects of Weight...can compare the sample data. The dataset of this project has the reference population on unit level for group and gender, which is in red-dotted box

An Approach for Implementing a Microcomputer Based Report Origination System in the Ada Programming Language

DTIC Science & Technology

1983-03-01

Decision Tree -------------------- 62 4-E. PACKAGE unitrep Action/Area Selection flow Chart 82 4-7. PACKAGE unitrep Control Flow Chart...the originetor wculd manually draft simple, readable, formatted iressages using "-i predef.ined forms and decision logic trees . This alternative was...Study Analysis DATA CCNTENT ERRORS PERCENT OF ERRORS Character Type 2.1 Calcvlations/Associations 14.3 Message Identification 4.? Value Pisiratch 22.E

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spatially filtered to enforce local consensus among neighboring pixels and the spatially filtered image is output.

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spacially filtered to enforce local consensus among neighboring pixels and the spacially filtered image is output.

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China.

PubMed

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-11-09

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities-for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals-were the main external sources of a large amount of Hg in the farmland soil.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

Identifying Risk and Protective Factors in Recidivist Juvenile Offenders: A Decision Tree Approach

PubMed Central

Ortega-Campos, Elena; García-García, Juan; Gil-Fenoy, Maria José; Zaldívar-Basurto, Flor

2016-01-01

Research on juvenile justice aims to identify profiles of risk and protective factors in juvenile offenders. This paper presents a study of profiles of risk factors that influence young offenders toward committing sanctionable antisocial behavior (S-ASB). Decision tree analysis is used as a multivariate approach to the phenomenon of repeated sanctionable antisocial behavior in juvenile offenders in Spain. The study sample was made up of the set of juveniles who were charged in a court case in the Juvenile Court of Almeria (Spain). The period of study of recidivism was two years from the baseline. The object of study is presented, through the implementation of a decision tree. Two profiles of risk and protective factors are found. Risk factors associated with higher rates of recidivism are antisocial peers, age at baseline S-ASB, problems in school and criminality in family members. PMID:27611313

Circum-Arctic petroleum systems identified using decision-tree chemometrics

USGS Publications Warehouse

Peters, K.E.; Ramos, L.S.; Zumberge, J.E.; Valin, Z.C.; Scotese, C.R.; Gautier, D.L.

2007-01-01

Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China

PubMed Central

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-01-01

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities—for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals—were the main external sources of a large amount of Hg in the farmland soil. PMID:27834884

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Testing the Effectiveness of the North Shore - LIJ Health System’s Bioterrorism Response Program to Identified Surveillance Data

DTIC Science & Technology

2007-03-01

Enteritis GI 008.5 ENTERITIS, BACTERIAL NOS Enteritis GI 008.6 ENTERITIS D/T SPECIFIED V Enteritis GI 008.61 ENTERITIS D/T ROTAVIRUS Enteritis GI...008.61 ENTERITIS D/T ROTAVIRUS Enteritis GI 008.62 ENTERITIS D/T ADENOVIRUS Enteritis GI 008.63 ENTERITIS D/T NORWALK VIR Enteritis GI 008.64

Modified method for estimating petroleum source-rock potential using wireline logs, with application to the Kingak Shale, Alaska North Slope

USGS Publications Warehouse

Rouse, William A.; Houseknecht, David W.

2016-02-11

In 2012, the U.S. Geological Survey completed an assessment of undiscovered, technically recoverable oil and gas resources in three source rocks of the Alaska North Slope, including the lower part of the Jurassic to Lower Cretaceous Kingak Shale. In order to identify organic shale potential in the absence of a robust geochemical dataset from the lower Kingak Shale, we introduce two quantitative parameters, $\\Delta DT_\\bar{x}$ and $\\Delta DT_z$, estimated from wireline logs from exploration wells and based in part on the commonly used delta-log resistivity ($\\Delta \\text{ }log\\text{ }R$) technique. Calculation of $\\Delta DT_\\bar{x}$ and $\\Delta DT_z$ is intended to produce objective parameters that may be proportional to the quality and volume, respectively, of potential source rocks penetrated by a well and can be used as mapping parameters to convey the spatial distribution of source-rock potential. Both the $\\Delta DT_\\bar{x}$ and $\\Delta DT_z$ mapping parameters show increased source-rock potential from north to south across the North Slope, with the largest values at the toe of clinoforms in the lower Kingak Shale. Because thermal maturity is not considered in the calculation of $\\Delta DT_\\bar{x}$ or $\\Delta DT_z$, total organic carbon values for individual wells cannot be calculated on the basis of $\\Delta DT_\\bar{x}$ or $\\Delta DT_z$ alone. Therefore, the $\\Delta DT_\\bar{x}$ and $\\Delta DT_z$ mapping parameters should be viewed as first-step reconnaissance tools for identifying source-rock potential.

Tree value system: users guide.

Treesearch

J.K. Ayer Sachet; D.G. Briggs; R.D. Fight

1989-01-01

This paper instructs resource analysts on use of the Tree Value System (TREEVAL). TREEVAL is a microcomputer system of programs for calculating tree or stand values and volumes based on predicted product recovery. Designed for analyzing silvicultural decisions, the system can also be used for appraisals and for evaluating log bucking. The system calculates results...

A decision support tool for identifying abuse of controlled substances by ForwardHealth Medicaid members.

PubMed

Mailloux, Allan T; Cummings, Stephen W; Mugdh, Mrinal

2010-01-01

Our objective was to use Wisconsin's Medicaid Evaluation and Decision Support (MEDS) data warehouse to develop and validate a decision support tool (DST) that (1) identifies Wisconsin Medicaid fee-for-service recipients who are abusing controlled substances, (2) effectively replicates clinical pharmacist recommendations for interventions intended to curb abuse of physician and pharmacy services, and (3) automates data extraction, profile generation and tracking of recommendations and interventions. From pharmacist manual reviews of medication profiles, seven measures of overutilization of controlled substances were developed, including (1-2) 6-month and 2-month "shopping" scores, (3-4) 6-month and 2-month forgery scores, (5) duplicate/same day prescriptions, (6) count of controlled substance claims, and the (7) shopping 6-month score for the individual therapeutic class with the highest score. The pattern analysis logic for the measures was encoded into SQL and applied to the medication profiles of 190 recipients who had already undergone manual review. The scores for each measure and numbers of providers were analyzed by exhaustive chi-squared automatic interaction detection (CHAID) to determine significant thresholds and combinations of predictors of pharmacist recommendations, resulting in a decision tree to classify recipients by pharmacist recommendations. The overall correct classification rate of the decision tree was 95.3%, with a 2.4% false positive rate and 4.0% false negative rate for lock-in versus prescriber-alert letter recommendations. Measures used by the decision tree include the 2-month and 6-month shopping scores, and the number of pharmacies and prescribers. The number of pharmacies was the best predictor of abuse of controlled substances. When a Medicaid recipient receives prescriptions for controlled substances at 8 or more pharmacies, the likelihood of a lock-in recommendation is 90%. The availability of the Wisconsin MEDS data warehouse has enabled development and application of a decision tree for detecting recipient fraud and abuse of controlled substance medications. Using standard pharmacy claims data, the decision tree effectively replicates pharmacist manual review recommendations. The DST has automated extraction and evaluation of pharmacy claims data for creating recommendations for guiding pharmacists in the selection of profiles for manual review. The DST is now the primary method used by the Wisconsin Medicaid program to detect fraud and abuse of physician and pharmacy services committed by recipients.

A decision support system using combined-classifier for high-speed data stream in smart grid

NASA Astrophysics Data System (ADS)

Yang, Hang; Li, Peng; He, Zhian; Guo, Xiaobin; Fong, Simon; Chen, Huajun

2016-11-01

Large volume of high-speed streaming data is generated by big power grids continuously. In order to detect and avoid power grid failure, decision support systems (DSSs) are commonly adopted in power grid enterprises. Among all the decision-making algorithms, incremental decision tree is the most widely used one. In this paper, we propose a combined classifier that is a composite of a cache-based classifier (CBC) and a main tree classifier (MTC). We integrate this classifier into a stream processing engine on top of the DSS such that high-speed steaming data can be transformed into operational intelligence efficiently. Experimental results show that our proposed classifier can return more accurate answers than other existing ones.

Synthesis of a novel reactive flame retardant containing phosphaphenanthrene and triazine-trione groups and its application in unsaturated polyester resin

NASA Astrophysics Data System (ADS)

Huo, Siqi; Wang, Jun; Yang, Shuang; Cai, Haopeng; Zhang, Bin; Chen, Xi; Wu, Qilei; Yang, Lingfeng

2018-03-01

A new-type compound (DT) which contained phosphaphenanthrene and triazine-trione groups was synthesized. DT was served as a reactive flame retardant for unsaturated polyester resin (UP). The thermal degradation, flame-retarded and mechanical properties of UP/DT samples were detected by different tests. According to the results, the addition of DT improved the initial thermal decomposition temperature (T5% and T10%) and the char yields of UP thermosets. Additionally, incorporation of DT resulted in the decrease of flexural and tensile strength of UP samples, and the increase of flexural modulus. The flame-retarded performance of UP/DT samples was greatly improved compared with the neat UP thermoset. For instance, the limited oxygen index (LOI) and vertical burning (UL94) rating of UP/DT-30 sample with 30 wt% DT came up to 29.8% and V-1. In comparison to pure UP thermoset, the average of heat release rate (av-HRR), total heat release (THR) and average of effective heat of combustion (av-EHC) of UP/DT-30 thermoset were decreased by 35.9%, 31.2% and 29.1%, respectively. Phosphaphenanthrene and triazine-trione groups in DT synergistically enhanced flame-retarded capability of UP in both gas phase and condensed phase.

DT-13 attenuates human lung cancer metastasis via regulating NMIIA activity under hypoxia condition.

PubMed

Wei, Xiao-Hui; Lin, Sen-Sen; Liu, Yang; Zhao, Ren-Ping; Khan, Ghulam Jilany; Du, Hong-Zhi; Mao, Ting-Ting; Yu, Bo-Yang; Li, Rui-Ming; Yuan, Sheng-Tao; Sun, Li

2016-08-01

Cancer metastasis plays a major role in tumor deterioration. Metastatic processes are known to be regulated by hypoxic microenvironment and non-muscle myosin IIA (NMIIA). DT-13, a bioactive saponin monomer isolated from Ophiopogon japonicus, has been reported to inhibit various cancer metastasis, but whether NMIIA is involved in the anti-metastatic activity of DT-13 under hypoxia remains to be determined. Thus, this study aims to clarify the role of DT-13 in regulating 95D cell metastasis under hypoxic microenvironment and to further investigate whether NMIIA is involved in the anti-metastatic mechanism of DT-13. We found that DT-13 significantly inhibited 95D cells metastasis in vitro and in vivo. Furthermore, hypoxia significantly inhibited the expression of NMIIA and redistributed NMIIA to the cell periphery, whereas DT-13 reversed the hypoxic effects by upregulating the expression of NMIIA. Moreover, DT-13 treatment redistributed NMIIA to the nuclear periphery and reduced the formation of F-actin in 95D cells. In addition, we found that the Raf-ERK1/2 signaling pathway is involved in regulation of NMIIA by DT-13. Collectively, these findings support NMIIA as a target of DT-13 to prevent lung cancer metastasis.

The saponin monomer of dwarf lilyturf tuber, DT-13, reduces L-type calcium currents during hypoxia in adult rat ventricular myocytes.

PubMed

Tao, Jin; Wang, Hongyi; Zhou, Hong; Li, Shengnan

2005-10-28

The saponin monomer 13 of dwarf lilyturf tuber (DT-13), one of the saponin monomers of dwarf lilyturf tuber, has been found to have potent cardioprotective effects. In order to investigate the effects of DT-13 on L-type calcium currents (I(Ca,L)), exploring the mechanisms of DT-13's cardioprotective effects in the condition of pathophysiology, we directly measured the I(Ca,L) during hypoxia in the adult rat cardiac myocytes exposed to DT-13 using standard whole-cell patch-clamp recording technique. Our previous results showed that DT-13 exerted decreasing effects on the I(Ca,L) of the single adult rat cardiac myocytes. In the condition of hypoxia, the current density was inhibited by about 29% after exposure of the cells to DT-13 (0.1 micromol L(-1)) for 10 min, from 6.96+/-1.05 pA/pF to 4.38+/-0.35 pA/pF (n=5, P<0.05). This I(Ca,L)-inhibiting action of DT-13 was concentration-dependent and showed no frequency-dependence. DT-13 up-shifted the current-voltage (I-V) curve. Steady-state activation of I(Ca,L) was not affected markedly, and the half activation potential (V(0.5)) in the presence of DT-13 (0.1 micromol L(-1)) was also not significantly different. DT-13 at 0.1 micromol L(-1) markedly accelerated the voltage-dependent steady-state inactivation of calcium current and shifted the steady-state inactivation curve of I(Ca,L) to the left. In combination with previous reports, these results suggest that there might be a close relationship between the cardioprotective effects of DT-13 and L-type calcium channels in the condition of hypoxia.

Dignity Therapy and Life Review for Palliative Care Patients: A Randomized Controlled Trial.

PubMed

Vuksanovic, Dean; Green, Heather J; Dyck, Murray; Morrissey, Shirley A

2017-02-01

Dignity therapy (DT) is a psychotherapeutic intervention with increasing evidence of acceptability and utility in palliative care settings. The aim of this study was to evaluate the legacy creation component of DT by comparing this intervention with life review (LR) and waitlist control (WC) groups. Seventy adults with advanced terminal disease were randomly allocated to DT, LR, or WC followed by DT, of which 56 completed the study protocol. LR followed an identical protocol to DT except that no legacy document was created in LR. Primary outcome measures were the Brief Generativity and Ego-Integrity Questionnaire, Patient Dignity Inventory, Functional Assessment of Cancer Therapy-General, version 4, and treatment evaluation questionnaires. Unlike LR and WC groups, DT recipients demonstrated significantly increased generativity and ego-integrity scores at study completion. There were no significant changes for dignity-related distress or physical, social, emotional, and functional well-being among the three groups. There were also no significant changes in primary outcomes after the provision of DT after the waiting period in the WC group. High acceptability and satisfaction with interventions were noted for recipients of both DT and LR and family/carers of DT participants. This study provides initial evidence that the specific process of legacy creation is able to positively affect sense of generativity, meaning, and acceptance near end of life. High acceptability and satisfaction rates for both DT and LR and positive impacts on families/carers of DT participants provide additional support for clinical utility of these interventions. Further evaluation of specific mechanisms of change post-intervention is required given DT's uncertain efficacy on other primary outcomes. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Risk for self-reported anorexia or bulimia nervosa based on drive for thinness and negative affect clusters/dimensions during adolescence: A three-year prospective study of the TChAD cohort.

PubMed

Peñas-Lledó, Eva; Bulik, Cynthia M; Lichtenstein, Paul; Larsson, Henrik; Baker, Jessica H

2015-09-01

This study explored the cross-sectional and predictive effect of drive for thinness and/or negative affect scores on the development of self-reported anorexia nervosa (AN) and bulimia nervosa (BN). K-means were used to cluster the Eating Disorder Inventory-Drive for Thinness (DT) and Child Behavior Checklist Anxious/Depressed (A/D) scores from 615 unrelated female twins at age 16-17. Logistic regressions were used to assess the effect of these clusters on self-reported eating disorder diagnosis at ages 16-17 (n = 565) and 19-20 (n = 451). DT and A/D scores were grouped into four clusters: Mild (scores lower than 90th percentile on both scales), DT (higher scores only on DT), A/D (higher scores only on A/D), and DT-A/D (higher scores on both the DT and A/D scales). DT and DT-A/D clusters at age 16-17 were associated cross-sectionally with AN and both cross-sectionally and longitudinally with BN. The DT-A/D cluster had the highest prevalence of AN at follow-up compared with all other clusters. Similarly, an interaction was observed between DT and A/D that predicted risk for AN. Having elevated DT and A/D scores may increase risk for eating disorder symptomatology above and beyond a high score on either alone. Findings suggest that cluster modeling based on DT and A/D may be useful to inform novel and useful intervention strategies for AN and BN in adolescents. © 2015 Wiley Periodicals, Inc.

Risk for self-reported anorexia or bulimia nervosa based on drive for thinness and negative affect clusters/dimensions during adolescence: A three-year prospective study of the TChAD cohort

PubMed Central

Peñas-Lledó, Eva; Bulik, Cynthia M.; Lichtenstein, Paul; Larsson, Henrik; Baker, Jessica H.

2015-01-01

Objective The present study explored the cross-sectional and predictive effect of drive for thinness and/or negative affect scores on the development of self-reported anorexia nervosa (AN) and bulimia nervosa (BN). Method K-means were used to cluster the Eating Disorder Inventory-Drive for Thinness (DT) and Child Behavior Checklist Anxious/Depressed (A/D) scores from 615 unrelated female twins at age 16–17. Logistic regressions were used to assess the effect of these clusters on self-reported eating disorder diagnosis at ages 16–17 (n=565) and 19–20 (n=451). Results DT and A/D scores were grouped into four clusters: Mild (scores lower than 90th percentile on both scales), DT (higher scores only on DT), A/D (higher scores only on A/D), and DT-A/D (higher scores on both the DT and A/D scales). DT and DT-A/D clusters at age 16–17 were associated cross-sectionally with AN and both cross-sectionally and longitudinally with BN. The DT-A/D cluster had the highest prevalence of AN at follow-up compared with all other clusters. Similarly, an interaction was observed between DT and A/D that predicted risk for AN. Discussion Having elevated DT and A/D scores may increase risk for eating disorder symptomatology above and beyond a high score on either alone. Findings suggest that cluster modeling based on DT and A/D may be useful to inform novel and useful intervention strategies for AN and BN in adolescents. PMID:26013185

Enhancement of hypermutation frequency in the chicken B cell line DT40 for efficient diversification of the antibody repertoire

DOE Office of Scientific and Technical Information (OSTI.GOV)

Magari, Masaki; Kanehiro, Yuichi; Todo, Kagefumi

Chicken B cell line DT40 continuously accumulates mutations in the immunoglobulin variable region (IgV) gene by gene conversion and point mutation, both of which are mediated by activation-induced cytidine deaminase (AID), thereby producing an antibody (Ab) library that is useful for screening monoclonal Abs (mAbs) in vitro. We previously generated an engineered DT40 line named DT40-SW, whose AID expression can be reversibly switched on or off, and developed an in vitro Ab generation system using DT40-SW cells. To efficiently create an Ab library with sufficient diversity, higher hypermutation frequency is advantageous. To this end, we generated a novel cell linemore » DT40-SW{Delta}C, which conditionally expresses a C-terminus-truncated AID mutant lacking the nuclear export signal. The transcription level of the mutant AID gene in DT40-SW{Delta}C cells was similar to that of the wild-type gene in DT40-SW cells. However, the protein level of the truncated AID mutant was less than that of the wild type. The mutant protein was enriched in the nuclei of DT40-SW{Delta}C cells, although the protein might be highly susceptible to degradation. In DT40-SW{Delta}C cells, both gene conversion and point mutation occurred in the IgV gene with over threefold higher frequency than in DT40-SW cells, suggesting that a lower level of the mutant AID protein was sufficient to increase mutation frequency. Thus, DT40-SW{Delta}C cells may be useful for constructing Ab libraries for efficient screening of mAbs in vitro.« less

Advanced Subspace Techniques for Modeling Channel and Session Variability in a Speaker Recognition System

DTIC Science & Technology

2012-03-01

with each SVM discriminating between a pair of the N total speakers in the data set. The (( + 1))/2 classifiers then vote on the final...classification of a test sample. The Random Forest classifier is an ensemble classifier that votes amongst decision trees generated with each node using...Forest vote , and the effects of overtraining will be mitigated by the fact that each decision tree is overtrained differently (due to the random

The association of a high drive for thinness with energy deficiency and severe menstrual disturbances: confirmation in a large population of exercising women.

PubMed

Gibbs, Jenna C; Williams, Nancy I; Scheid, Jennifer L; Toombs, Rebecca J; De Souza, Mary Jane

2011-08-01

A high drive-for-thinness (DT) score obtained from the Eating Disorder Inventory-2 is associated with surrogate markers of energy deficiency in exercising women. The purposes of this study were to confirm the association between DT and energy deficiency in a larger population of exercising women that was previously published and to compare the distribution of menstrual status in exercising women when categorized as high vs. normal DT. A high DT was defined as a score ≥7, corresponding to the 75th percentile for college-age women. Exercising women age 22.9 ± 4.3 yr with a BMI of 21.2 ± 2.2 kg/m2 were retrospectively grouped as high DT (n = 27) or normal DT (n = 90) to compare psychometric, energetic, and reproductive characteristics. Chi-square analyses were performed to compare the distribution of menstrual disturbances between groups. Measures of resting energy expenditure (REE) (4,949 ± 494 kJ/day vs. 5,406 ± 560 kJ/day, p < .001) and adjusted REE (123 ± 16 kJ/LBM vs. 130 ± 9 kJ/LBM, p = .027) were suppressed in exercising women with high DT vs. normal DT, respectively. Ratio of measured REE to predicted REE (pREE) in the high-DT group was 0.85 ± 0.10, meeting the authors' operational definition for an energy deficiency (REE:pREE <0.90). A greater prevalence of severe menstrual disturbances such as amenorrhea and oligomenorrhea was observed in the high-DT group (χ2 = 9.3, p = .003) than in the normal-DT group. The current study confirms the association between a high DT score and energy deficiency in exercising women and demonstrates a greater prevalence of severe menstrual disturbances in exercising women with high DT.

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

NASA Astrophysics Data System (ADS)

Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

2017-03-01

Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

Systematic Review of Clinical Outcomes Following Various Treatment Options for Patients with Extraabdominal Desmoid Tumors.

PubMed

Smith, Kortnye; Desai, Jayesh; Lazarakis, Smaro; Gyorki, David

2018-06-01

Desmoid tumors (DT) are rare clonal proliferations that arise from mesenchymal cells. These tumors do not metastasize but are locally aggressive, and their growth may lead to significant morbidity. Their clinical course is both variable and unpredictable; tumors may rapidly progress but in other instances remain stable or regress without intervention. To examine current treatment of DT and assist with decision-making at time of presentation. A literature search was conducted of MEDLINE and Cochrane databases for published studies (1995-July 2015) using the search terms fibromatosis aggressive, desmoid with drug therapy, radiation therapy, prevention and control, radiotherapy, surgery, and therapy. Articles were categorized as surgery, radiation, surgery + radiation, systemic therapy, and front-line observation. Articles were included if they reported a retrospective or prospective comparative or observational study with an analyzed sample size of 10 patients or more with confirmed diagnosis of desmoid tumor and described one of the following clinical outcomes: relapse- or progression-free survival, local control rate, response rate. 258 articles were reviewed; following screening for eligibility, 54 were identified; following full-text screen, 31 were included in final evaluation. The control rate for patients treated with a "wait and see" observational approach compared favorably with management with surgery and resulted in disease control rates of between 60 and 92%. Decision-making in this rare tumor is complicated by the range of treatment options available. Our evidence supports use of an upfront observational approach.

A dynamic fault tree model of a propulsion system

NASA Technical Reports Server (NTRS)

Xu, Hong; Dugan, Joanne Bechta; Meshkat, Leila

2006-01-01

We present a dynamic fault tree model of the benchmark propulsion system, and solve it using Galileo. Dynamic fault trees (DFT) extend traditional static fault trees with special gates to model spares and other sequence dependencies. Galileo solves DFT models using a judicious combination of automatically generated Markov and Binary Decision Diagram models. Galileo easily handles the complexities exhibited by the benchmark problem. In particular, Galileo is designed to model phased mission systems.

Including public-health benefits of trees in urban-forestry decision making

Treesearch

Geoffrey H. Donovan

2017-01-01

Research demonstrating the biophysical benefits of urban trees are often used to justify investments in urban forestry. Far less emphasis, however, is placed on the non-bio-physical benefits such as improvements in public health. Indeed, the public-health benefits of trees may be significantly larger than the biophysical benefits, and, therefore, failure to account for...

Goal Programming: A New Tool for the Christmas Tree Industry

Treesearch

Bruce G. Hansen

1977-01-01

Goal programing (GP) can be useful for decision making in the natural Christmas tree industry. Its usefulness is demonstrated through an analysis of a hypothetical problem in which two potential growers decide how to use 10 acres in growing Christmas trees. Though the physical settings are identical, distinct differences between their goals significantly influence the...

Lessons learned from Applications of a Decision Tree for Confronting Climate Change Uncertainty - the Short Term and the Long Term

NASA Astrophysics Data System (ADS)

Ray, P. A.; Wi, S.; Bonzanigo, L.; Taner, M. U.; Rodriguez, D.; Garcia, L.; Brown, C.

2016-12-01

The Decision Tree for Confronting Climate Change Uncertainty is a hierarchical, staged framework for accomplishing climate change risk management in water resources system investments. Since its development for the World Bank Water Group two years ago, the framework has been applied to pilot demonstration projects in Nepal (hydropower generation), Mexico (water supply), Kenya (multipurpose reservoir operation), and Indonesia (flood risks to dam infrastructure). An important finding of the Decision Tree demonstration projects has been the need to present the risks/opportunities of climate change to stakeholders and investors in proportion to risks/opportunities and hazards of other kinds. This presentation will provide an overview of tools and techniques used to quantify risks/opportunities to each of the project types listed above, with special attention to those found most useful for exploration of the risk space. Careful exploration of the risk/opportunity space shows that some interventions would be better taken now, whereas risks/opportunities of other types would be better instituted incrementally in order to maintain reversibility and flexibility. A number of factors contribute to the robustness/flexibility tradeoff: available capital, magnitude and imminence of potential risk/opportunity, modular (or not) character of investment, and risk aversion of the decision maker, among others. Finally, in each case, nuance was required in the translation of Decision Tree findings into actionable policy recommendations. Though the narrative of stakeholder solicitation, engagement, and ultimate partnership is unique to each case, summary lessons are available from the portfolio that can serve as a guideline to the community of climate change risk managers.

Decision tree analysis of treatment strategies for mild and moderate cases of clinical mastitis occurring in early lactation.

PubMed

Pinzón-Sánchez, C; Cabrera, V E; Ruegg, P L

2011-04-01

The objective of this study was to develop a decision tree to evaluate the economic impact of different durations of intramammary treatment for the first case of mild or moderate clinical mastitis (CM) occurring in early lactation with various scenarios of pathogen distributions and use of on-farm culture. The tree included 2 decision and 3 probability events. The first decision evaluated use of on-farm culture (OFC; 2 programs using OFC and 1 not using OFC) and the second decision evaluated treatment strategies (no intramammary antimicrobials or antimicrobials administered for 2, 5, or 8 d). The tree included probabilities for the distribution of etiologies (gram-positive, gram-negative, or no growth), bacteriological cure, and recurrence. The economic consequences of mastitis included costs of diagnosis and initial treatment, additional treatments, labor, discarded milk, milk production losses due to clinical and subclinical mastitis, culling, and transmission of infection to other cows (only for CM caused by Staphylococcus aureus). Pathogen-specific estimates for bacteriological cure and milk losses were used. The economically optimal path for several scenarios was determined by comparison of expected monetary values. For most scenarios, the optimal economic strategy was to treat CM caused by gram-positive pathogens for 2 d and to avoid antimicrobials for CM cases caused by gram-negative pathogens or when no pathogen was recovered. Use of extended intramammary antimicrobial therapy (5 or 8 d) resulted in the least expected monetary values. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

PubMed

Pitcher, Brandon; Alaqla, Ali; Noujeim, Marcel; Wealleans, James A; Kotsakis, Georgios; Chrepa, Vanessa

2017-03-01

Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm 3 , there was 80% probability of a cyst. If volume was <247 mm 3 and root displacement was present, cyst probability was 60% (78% accuracy). The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.

Rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems in the presence of trees.

PubMed

Scholz, Miklas; Uzomah, Vincent C

2013-08-01

The retrofitting of sustainable drainage systems (SuDS) such as permeable pavements is currently undertaken ad hoc using expert experience supported by minimal guidance based predominantly on hard engineering variables. There is a lack of practical decision support tools useful for a rapid assessment of the potential of ecosystem services when retrofitting permeable pavements in urban areas that either feature existing trees or should be planted with trees in the near future. Thus the aim of this paper is to develop an innovative rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems close to trees. This unique tool proposes the retrofitting of permeable pavements that obtained the highest ecosystem service score for a specific urban site enhanced by the presence of trees. This approach is based on a novel ecosystem service philosophy adapted to permeable pavements rather than on traditional engineering judgement associated with variables based on quick community and environment assessments. For an example case study area such as Greater Manchester, which was dominated by Sycamore and Common Lime, a comparison with the traditional approach of determining community and environment variables indicates that permeable pavements are generally a preferred SuDS option. Permeable pavements combined with urban trees received relatively high scores, because of their great potential impact in terms of water and air quality improvement, and flood control, respectively. The outcomes of this paper are likely to lead to more combined permeable pavement and tree systems in the urban landscape, which are beneficial for humans and the environment. Copyright © 2013 Elsevier B.V. All rights reserved.

Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.

PubMed

Hu, Jianfeng

2017-01-01

Purpose: Driving fatigue has become one of the important causes of road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in the measuring fatigue state. Manual interpretation of EEG signals is impossible, so an effective method for automatic detection of EEG signals is crucial needed. Method: In order to evaluate the complex, unstable, and non-linear characteristics of EEG signals, four feature sets were computed from EEG signals, in which fuzzy entropy (FE), sample entropy (SE), approximate Entropy (AE), spectral entropy (PE), and combined entropies (FE + SE + AE + PE) were included. All these feature sets were used as the input vectors of AdaBoost classifier, a boosting method which is fast and highly accurate. To assess our method, several experiments including parameter setting and classifier comparison were conducted on 28 subjects. For comparison, Decision Trees (DT), Support Vector Machine (SVM) and Naive Bayes (NB) classifiers are used. Results: The proposed method (combination of FE and AdaBoost) yields superior performance than other schemes. Using FE feature extractor, AdaBoost achieves improved area (AUC) under the receiver operating curve of 0.994, error rate (ERR) of 0.024, Precision of 0.969, Recall of 0.984, F1 score of 0.976, and Matthews correlation coefficient (MCC) of 0.952, compared to SVM (ERR at 0.035, Precision of 0.957, Recall of 0.974, F1 score of 0.966, and MCC of 0.930 with AUC of 0.990), DT (ERR at 0.142, Precision of 0.857, Recall of 0.859, F1 score of 0.966, and MCC of 0.716 with AUC of 0.916) and NB (ERR at 0.405, Precision of 0.646, Recall of 0.434, F1 score of 0.519, and MCC of 0.203 with AUC of 0.606). It shows that the FE feature set and combined feature set outperform other feature sets. AdaBoost seems to have better robustness against changes of ratio of test samples for all samples and number of subjects, which might therefore aid in the real-time detection of driver fatigue through the classification of EEG signals. Conclusion: By using combination of FE features and AdaBoost classifier to detect EEG-based driver fatigue, this paper ensured confidence in exploring the inherent physiological mechanisms and wearable application.

Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals

PubMed Central

Hu, Jianfeng

2017-01-01

Purpose: Driving fatigue has become one of the important causes of road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in the measuring fatigue state. Manual interpretation of EEG signals is impossible, so an effective method for automatic detection of EEG signals is crucial needed. Method: In order to evaluate the complex, unstable, and non-linear characteristics of EEG signals, four feature sets were computed from EEG signals, in which fuzzy entropy (FE), sample entropy (SE), approximate Entropy (AE), spectral entropy (PE), and combined entropies (FE + SE + AE + PE) were included. All these feature sets were used as the input vectors of AdaBoost classifier, a boosting method which is fast and highly accurate. To assess our method, several experiments including parameter setting and classifier comparison were conducted on 28 subjects. For comparison, Decision Trees (DT), Support Vector Machine (SVM) and Naive Bayes (NB) classifiers are used. Results: The proposed method (combination of FE and AdaBoost) yields superior performance than other schemes. Using FE feature extractor, AdaBoost achieves improved area (AUC) under the receiver operating curve of 0.994, error rate (ERR) of 0.024, Precision of 0.969, Recall of 0.984, F1 score of 0.976, and Matthews correlation coefficient (MCC) of 0.952, compared to SVM (ERR at 0.035, Precision of 0.957, Recall of 0.974, F1 score of 0.966, and MCC of 0.930 with AUC of 0.990), DT (ERR at 0.142, Precision of 0.857, Recall of 0.859, F1 score of 0.966, and MCC of 0.716 with AUC of 0.916) and NB (ERR at 0.405, Precision of 0.646, Recall of 0.434, F1 score of 0.519, and MCC of 0.203 with AUC of 0.606). It shows that the FE feature set and combined feature set outperform other feature sets. AdaBoost seems to have better robustness against changes of ratio of test samples for all samples and number of subjects, which might therefore aid in the real-time detection of driver fatigue through the classification of EEG signals. Conclusion: By using combination of FE features and AdaBoost classifier to detect EEG-based driver fatigue, this paper ensured confidence in exploring the inherent physiological mechanisms and wearable application. PMID:28824409

The maximal downstroke of epicardial potentials as an index of electrical activity in mouse hearts.

PubMed

Sohn, Kwanghyun; Sachse, Frank B; Moreno, Alonso P; Ershler, Philip R; Wende, Adam R; Abel, E Dale; Punske, Bonnie B

2011-11-01

The maximal upstroke of transmembrane voltage (dV(m)/dt(max)) has been used as an indirect measure of sodium current I(Na) upon activation in cardiac myocytes. However, sodium influx generates not only the upstroke of V(m), but also the downstroke of the extracellular potentials V(e) including epicardial surface potentials V(es). The purpose of this study was to evaluate the magnitude of the maximal downstroke of V(es) (|dV(es)/dt (min)|) as a global index of electrical activation, based on the relationship of dV(m)/dt(max) to I(Na). To fulfill this purpose, we examined |dV(es)/dt(min)| experimentally using isolated perfused mouse hearts and computationally using a 3-D cardiac tissue bidomain model. In experimental studies, a custom-made cylindrical "cage" array with 64 electrodes was slipped over mouse hearts to measure V(es) during hyperkalemia, ischemia, and hypoxia, which are conditions that decrease I(Na). Values of |dV(es)/dt(min)| from each electrode were normalized (|dV(es)/dt (min)|(n)) and averaged (|dV(es)/dt(min)|(na)). Results showed that |dV(es)/dt(min)|(na) decreased during hyperkalemia by 28, 59, and 79% at 8, 10, and 12 mM [K(+)](o), respectively. |dV(es)/dt(min)| also decreased by 54 and 84% 20 min after the onset of ischemia and hypoxia, respectively. In computational studies, |dV(es)/dt(min)| was compared to dV(m)/dt(max) at different levels of the maximum sodium conductance G(Na), extracellular potassium ion concentration [K(+)](o), and intracellular sodium ion concentration [Na(+)](i), which all influence levels of I(Na). Changes in |dV(es)/dt(min)|(n) were similar to dV(m)/dt (max) during alterations of G(Na), [K(+)](o), and [Na(+)](i). Our results demonstrate that |dV(es)/dt(min)|(na) is a robust global index of electrical activation for use in mouse hearts and, similar to dV(m)/dt(max), can be used to probe electrophysiological alterations reliably. The index can be readily measured and evaluated, which makes it attractive for characterization of, for instance, genetically modified mouse hearts and drug effects on cardiac tissue.

Recent Additions for 1998

EPA Science Inventory

December 22, 1998

Benchmark Dose Software

December 16, 1998

Recent Additions for 1997

EPA Science Inventory

December 15, 1997

Minutes of the Stakeholder Meetings on the Report of the JSA Shrimp Virus Work Group

November 21, 1997

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elter, M.; Schulz-Wendtland, R.; Wittenberg, T.

2007-11-15

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predictmore » breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value <0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z)=0.89{+-}0.01, the decision-tree approach in A(z)=0.87{+-}0.01, and the ANN approach in A(z)=0.88{+-}0.01.« less

DT&E Forum for Best Practices and Lessons Learned

DTIC Science & Technology

2013-05-01

E A N A L Y S E S IDA Paper P-4975 DT&E Forum for Best Practices and Lessons Learned L. B. Scheiber, Project Leader...and accessing from the DT&E Forum website. A. Collection of Lessons Learned and Best Practices We began the effort by reviewing approximately 30...Forum’s Home Page 1. Searching for BPLL Documents The DT&E Forum website contains DT&E Best Practice and Lessons Learned (BPLL) documents along with the

Dt2 Is a Gain-of-Function MADS-Domain Factor Gene That Specifies Semideterminacy in Soybean[C][W

PubMed Central

Ping, Jieqing; Liu, Yunfeng; Sun, Lianjun; Zhao, Meixia; Li, Yinghui; She, Maoyun; Sui, Yi; Lin, Feng; Liu, Xiaodong; Tang, Zongxiang; Nguyen, Hanh; Tian, Zhixi; Qiu, Lijuan; Nelson, Randall L.; Clemente, Thomas E.; Specht, James E.; Ma, Jianxin

2014-01-01

Similar to Arabidopsis thaliana, the wild soybeans (Glycine soja) and many cultivars exhibit indeterminate stem growth specified by the shoot identity gene Dt1, the functional counterpart of Arabidopsis TERMINAL FLOWER1 (TFL1). Mutations in TFL1 and Dt1 both result in the shoot apical meristem (SAM) switching from vegetative to reproductive state to initiate terminal flowering and thus produce determinate stems. A second soybean gene (Dt2) regulating stem growth was identified, which, in the presence of Dt1, produces semideterminate plants with terminal racemes similar to those observed in determinate plants. Here, we report positional cloning and characterization of Dt2, a dominant MADS domain factor gene classified into the APETALA1/SQUAMOSA (AP1/SQUA) subfamily that includes floral meristem (FM) identity genes AP1, FUL, and CAL in Arabidopsis. Unlike AP1, whose expression is limited to FMs in which the expression of TFL1 is repressed, Dt2 appears to repress the expression of Dt1 in the SAMs to promote early conversion of the SAMs into reproductive inflorescences. Given that Dt2 is not the gene most closely related to AP1 and that semideterminacy is rarely seen in wild soybeans, Dt2 appears to be a recent gain-of-function mutation, which has modified the genetic pathways determining the stem growth habit in soybean. PMID:25005919

Clinical outcome and intraoperative neurophysiology for focal limb dystonic tremor without generalized dystonia treated with deep brain stimulation.

PubMed

Ramirez-Zamora, Adolfo; Kaszuba, Brian; Gee, Lucy; Prusik, Julia; Molho, Eric; Wilock, Meghan; Shin, Damian; Pilitsis, Julie G

2016-11-01

Dystonic tremor (DT) is defined as a postural/kinetic tremor occurring in the body region affected by dystonia. DT is typically characterized by focal tremors with irregular amplitudes and variable frequencies typically below 7Hz. Pharmacological treatment is generally unsuccessful and guidelines for deep brain stimulation (DBS) targeting and indications are scarce. In this article, we present the outcome and neurophysiologic data of two patients with refractory, focal limb DT treated with Globus Pallidus interna (Gpi) DBS and critically review the current literature regarding surgical treatment of DT discussing stereotactic targets and treatment considerations. A search of literature concerning treatment of DT was conducted. Additionally, Gpi DBS was performed in two patients with DT and microelectrode recordings for multi unit analysis (MUAs) and local field potentials (LFPs) were obtained. The mean percentage improvement in tremor severity was 80.5% at 3 years follow up. MUAs and LFPs did not show significant differences in DT patients compared with other forms of dystonia or PD except for higher interspikes bursting indices. LFP recordings in DT demonstrated high power at low frequencies with action (<3.5Hz). Gpi DBS is an effective treatment in patients with focal limb DT without associated generalized dystonia. Intraoperative neurophysiologic findings suggest that DT is part of phenotypic motor manifestations in dystonia. Copyright © 2016 Elsevier B.V. All rights reserved.

Desiccation tolerance of Sphagnum revisited: a puzzle resolved.

PubMed

Hájek, T; Vicherová, E

2014-07-01

As ecosystem engineers, Sphagnum mosses control their surroundings through water retention, acidification and peat accumulation. Because water retention avoids desiccation, sphagna are generally intolerant to drought; however, the literature on Sphagnum desiccation tolerance (DT) provides puzzling results, indicating the inducible nature of their DT. To test this, various Sphagnum species and other mesic bryophytes were hardened to drought by (i) slow drying; (ii) ABA application and (iii) chilling or frost. DT tolerance was assessed as recovery of chlorophyll fluorescence parameters after severe desiccation. We monitored the seasonal course of DT in bog bryophytes. Under laboratory conditions, following initial de-hardening, untreated Sphagnum shoots lacked DT; however, DT was induced by all hardening treatments except chilling, notably by slow drying, and in Sphagnum species of the section Cuspidata. In the field, sphagna in hollows and lawns developed DT several times during the growing season, responding to reduced precipitation and a lowered water table. Hummock and aquatic species developed DT only in late autumn, probably as a response to frost. Sphagnum protonemata failed to develop DT; hence, desiccation may limit Sphagnum establishment in drier habitats with suitable substrate chemistry. Desiccation avoiders among sphagna form compact hummocks or live submerged; thus, they do not develop DT in the field, lacking the initial desiccation experience, which is frequent in hollow and lawn habitats. We confirmed the morpho-physiological trade-off: in contrast to typical hollow sphagna, hummock species invest more resources in water retention (desiccation avoidance), while they have a lower ability to develop physiological DT. © 2013 German Botanical Society and The Royal Botanical Society of the Netherlands.

Distillation time effect on lavender essential oil yield and composition.

PubMed

Zheljazkov, Valtcho D; Cantrell, Charles L; Astatkie, Tess; Jeliazkova, Ekaterina

2013-01-01

Lavender (Lavandula angustifolia Mill.) is one of the most widely grown essential oil crops in the world. Commercial extraction of lavender oil is done using steam distillation. The objective of this study was to evaluate the effect of the length of the distillation time (DT) on lavender essential oil yield and composition when extracted from dried flowers. Therefore, the following distillation times (DT) were tested in this experiment: 1.5 min, 3 min, 3.75 min, 7.5 min, 15 min, 30 min, 60 min, 90 min, 120 min, 150 min, 180 min, and 240 min. The essential oil yield (range 0.5-6.8%) reached a maximum at 60 min DT. The concentrations of cineole (range 6.4-35%) and fenchol (range 1.7-2.9%) were highest at the 1.5 min DT and decreased with increasing length of the DT. The concentration of camphor (range 6.6-9.2%) reached a maximum at 7.5-15 min DT, while the concentration of linalool acetate (range 15-38%) reached a maximum at 30 min DT. Results suggest that lavender essential oil yield may not increase after 60 min DT. The change in essential oil yield, and the concentrations of cineole, fenchol and linalool acetate as DT changes were modeled very well by the asymptotic nonlinear regression model. DT may be used to modify the chemical profile of lavender oil and to obtain oils with differential chemical profiles from the same lavender flowers. DT must be taken into consideration when citing or comparing reports on lavender essential oil yield and composition.

Application of Decision Tree to Obtain Optimal Operation Rules for Reservoir Flood Control Considering Sediment Desilting-Case Study of Tseng Wen Reservoir

NASA Astrophysics Data System (ADS)

ShiouWei, L.

2014-12-01

Reservoirs are the most important water resources facilities in Taiwan.However,due to the steep slope and fragile geological conditions in the mountain area,storm events usually cause serious debris flow and flood,and the flood then will flush large amount of sediment into reservoirs.The sedimentation caused by flood has great impact on the reservoirs life.Hence,how to operate a reservoir during flood events to increase the efficiency of sediment desilting without risk the reservoir safety and impact the water supply afterward is a crucial issue in Taiwan. 　Therefore,this study developed a novel optimization planning model for reservoir flood operation considering flood control and sediment desilting,and proposed easy to use operating rules represented by decision trees.The decision trees rules have considered flood mitigation,water supply and sediment desilting.The optimal planning model computes the optimal reservoir release for each flood event that minimum water supply impact and maximum sediment desilting without risk the reservoir safety.Beside the optimal flood operation planning model,this study also proposed decision tree based flood operating rules that were trained by the multiple optimal reservoir releases to synthesis flood scenarios.The synthesis flood scenarios consists of various synthesis storm events,reservoir's initial storage and target storages at the end of flood operating. 　Comparing the results operated by the decision tree operation rules(DTOR) with that by historical operation for Krosa Typhoon in 2007,the DTOR removed sediment 15.4% more than that of historical operation with reservoir storage only8.38×106m3 less than that of historical operation.For Jangmi Typhoon in 2008,the DTOR removed sediment 24.4% more than that of historical operation with reservoir storage only 7.58×106m3 less than that of historical operation.The results show that the proposed DTOR model can increase the sediment desilting efficiency and extend the reservoir life.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining.

PubMed

Habibi, Shafi; Ahmadi, Maryam; Alizadeh, Somayeh

2015-03-18

The aim of this study was to examine a predictive model using features related to the diabetes type 2 risk factors. The data were obtained from a database in a diabetes control system in Tabriz, Iran. The data included all people referred for diabetes screening between 2009 and 2011. The features considered as "Inputs" were: age, sex, systolic and diastolic blood pressure, family history of diabetes, and body mass index (BMI). Moreover, we used diagnosis as "Class". We applied the "Decision Tree" technique and "J48" algorithm in the WEKA (3.6.10 version) software to develop the model. After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the root node of the tree as a result of higher information gain. The ROC curve indicates the model function in identification of patients and those individuals who are healthy. The curve indicates high capability of the model, especially in identification of the healthy persons. We developed a model using the decision tree for screening T2DM which did not require laboratory tests for T2DM diagnosis.

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

PubMed

Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

Pricing and reimbursement frameworks in Central Eastern Europe: a decision tool to support choices.

PubMed

Kolasa, Katarzyna; Kalo, Zoltan; Hornby, Edward

2015-02-01

Given limited financial resources in the Central Eastern European (CEE) region, challenges in obtaining access to innovative medical technologies are formidable. The objective of this research was to develop a decision tree that supports decision makers and drug manufacturers from CEE region in their search for optimal innovative pricing and reimbursement scheme (IPRSs). A systematic literature review was performed to search for published IPRSs, and then ten experts from the CEE region were interviewed to ascertain their opinions on these schemes. In total, 33 articles representing 46 unique IPRSs were analyzed. Based on our literature review and subsequent expert input, key decision nodes and branches of the decision tree were developed. The results indicate that outcome-based schemes are better suited to deal with uncertainties surrounding cost effectiveness, while non-outcome-based schemes are more appropriate for pricing and budget impact challenges.

Development and Validation of a Primary Care-Based Family Health History and Decision Support Program (MeTree)

PubMed Central

Orlando, Lori A.; Buchanan, Adam H.; Hahn, Susan E.; Christianson, Carol A.; Powell, Karen P.; Skinner, Celette Sugg; Chesnut, Blair; Blach, Colette; Due, Barbara; Ginsburg, Geoffrey S.; Henrich, Vincent C.

2016-01-01

INTRODUCTION Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS Stakeholder feedback resulted in changes to MeTree’s interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree’s strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers’ needs. LIMITATIONS The tool was validated in a small cohort. CONCLUSION MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines. PMID:24044145

Age and education influence the performance of elderly women on the dual-task Timed Up and Go test.

PubMed

Gomes, Gisele de Cássia; Teixeira-Salmela, Luci Fuscaldi; Fonseca, Bruna Espeschit; Freitas, Flávia Alexandra Silveira de; Fonseca, Maria Luísa Morais; Pacheco, Bruna Débora; Gonçalves, Marisa Rocha; Caramelli, Paulo

2015-03-01

Gait variability is related to functional decline in the elderly. The dual-task Timed Up and Go Test (TUG-DT) reflects the performance in daily activities. Objective To evaluate the differences in time to perform the TUG with and without DT in elderly women with different ages and levels of education and physical activity. Method Ninety-two elderly women perfomed the TUG at usual and fast speeds, with and without motor and cognitive DT. Results Increases in the time to perform the TUG-DT were observed at older ages and lower educational levels, but not at different levels of physical activity. More educated women performed the test faster with and without DT at both speeds. When age was considered, significant differences were found only for the TUG-DT at both speeds. Conclusion Younger women with higher education levels demonstrated better performances on the TUG-DT.

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

PubMed

Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

2010-08-01

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predicting post-fire tree mortality for 12 western US conifers using the First-Order Fire Effects Model (FOFEM)

Treesearch

Sharon Hood; Duncan Lutes

2017-01-01

Accurate prediction of fire-caused tree mortality is critical for making sound land management decisions such as developing burning prescriptions and post-fire management guidelines. To improve efforts to predict post-fire tree mortality, we developed 3-year post-fire mortality models for 12 Western conifer species - white fir (Abies concolor [Gord. &...

Context-Sensitive Ethics in School Psychology

ERIC Educational Resources Information Center

Lasser, Jon; Klose, Laurie McGarry; Robillard, Rachel

2013-01-01

Ethical codes and licensing rules provide foundational guidance for practicing school psychologists, but these sources fall short in their capacity to facilitate effective decision-making. When faced with ethical dilemmas, school psychologists can turn to decision-making models, but step-wise decision trees frequently lack the situation…

SHOCKS Impulse-Jerk(I-J) Plasticity/Fracture Burst Acoustic-Emission(BAE) NON:``1''/ ω -``Noise'' Power-Law; Universality Power-Spectrum is I-J Time-Series Fourier-Transform: 1687 < < < 1988: VERY-LONG PRE-``Bak''!!!

NASA Astrophysics Data System (ADS)

Chavira, Aldo; Gregson, Victor, Jr.; Green, Sidney; Siegel, Edward

2011-06-01

SHOCKS impulse-jerk(I-J) [apply strain/impulse to get stress/jerk ],{VS. NON-shocks[apply stress to get strain]}, plasticity/fracture BAE[E. S.: MSE 8.,310(71); PSS: (a) 5, 601/607(71); Xl..-Latt. Defects 5, 277(74); Scripta Met.: 6, 785(72); 8, 587/617(74); 3rd Tokyo A.-E. Symp. (76);Acta Met.25,383(77); JMMM 7, 312(78)] NON: ``1''/ ω -``Noise'' Zipf(NON-Pareto); power-law ; universality power-spectrum is manifestly-demonstrated in ONLY ``PURE''-MATHS way to be nothing but d[F(t)=m(t)a(t)=Newton's (3rd) Law of Motion=(I-J)]/dt I-Jderivative d(I-J)/dt=dF(t)/dt=[m(t)da(t)/dt+a(t)dm(t)/dt] REdiscovery!!! A/Siegel NON-shock PHYSICS derivation fails!!!; ''PURE''-MATHS: dF(t)/dt=d2p(t)/dt2=[m(t)da(t)/dt+a(t)dm(t)/dt] TRIPLE-integral [VS. NON -shocks F = ma time-series DOUBLE-integral] Dichotomy: s(t) = [v0+(1/2)a(t)t2+EXTRA-TERM(S)], {VS. s(t) = [v0t+(1/2) at2]}, integral-transform formally defines power-spectrum Dichotomy:

Effectiveness of a flow-based device using riboflavin photochemistry in damaging blood-borne viral nucleic acids.

PubMed

Zhu, Liguo; Tong, Hongli; Wang, Shufang; Yu, Yang; Liu, Zhong; Li, Changqing; Wang, Deqing

2018-05-03

Effectiveness of a flow-based treatment device using riboflavin photochemistry was demonstrated by cytopathic effect method using indicator viruses. However, inactivation efficacy against real blood-borne viruses needs to be evaluated, especially at nucleic acid level. Special plasma samples with varying concentrations of blood-borne virus were selected using a strict blood selection procedure and were treated with device treatment (DT). Nucleic acid test (NAT) using polymerase chain reaction fluorescence method was used to detect virus copies. The NAT value of 4325 in plasma with high Hepatitis B Virus (HBV) concentrations decreased to 1330 with DT. After 100-fold dilution, the NAT value was below the NAT detection limits with DT compared with 23.0 that without DT. The NAT value of 61.9 in plasma with medium HBV concentrations decreased to 37.8 with DT, and after 10-fold dilution, the NAT value was below the NAT detection limits with DT compared with below 20 that without DT. The Ct values of plasma with low concentrations of blood-borne viruses were below the NAT detection limits with DT. There was a dose effect with DT which was effective in blood-borne viruses damaging nucleic acids to a level below the NAT detection limits. Copyright © 2018 Elsevier B.V. All rights reserved.

Diastolic blood pressure-estimated left ventricular dp/dt.

PubMed

Yilmaz, Hüseyin; Minareci, Kenan; Kabukçu, Mehmet; Sancaktar, Oktay

2002-02-01

Peak dp/dt is one of the best isovolumic phase indexes of the myocardial contractile state requiring invasive procedures or presence of mitral regurgitation severe enough to measure in clinical practice by Doppler echocardiography. In this study, we sought the correlation between two noninvasive methods of measurements for left ventricular dp/dt-diastolic blood pressure- (DBP) estimated and continuous-wave Doppler-derived dp/dt-min electrocardiographic/echocardiographic study to emphasize the clinical feasibility of the DBP-estimated method. Thirty-six randomized patients (27 male, 9 female; 58 +/- 8 years) with mild mitral regurgitation were enrolled in this study. DBP-estimated dp/dt was calculated from DBP minus the left ventricular end-diastolic pressure (LVEDP) over the isovolumetric contraction time (IVCT). LVEDP was assumed to be 10 mmHg for all patients. Doppler-determined left ventricular dp/dt was derived from the continuous-wave Doppler spectrum of mitral regurgitation jet by dividing the magnitude of the left ventricular atrial pressure gradient rise between 1 mm/sec-3 mm/sec of mitral regurgitant velocity signal by the time taken for this change. Left ventricular dp/dt by Doppler was 1122 +/- 303 mmHg/sec and blood pressure-estimated dp/dt was 1063 +/- 294 mmHg/sec. There was a high correlation (r = 0.97, P < 0.001) of dp/dt between the two techniques. DBP and IVCT can generate left ventricular dp/dt without invasive procedures, even in the absence of mitral regurgitation in clinical practice.

5-demethyltangeretin inhibits human nonsmall cell lung cancer cell growth by inducing G2/M cell cycle arrest and apoptosis.

PubMed

Charoensinphon, Noppawat; Qiu, Peiju; Dong, Ping; Zheng, Jinkai; Ngauv, Pearline; Cao, Yong; Li, Shiming; Ho, Chi-Tang; Xiao, Hang

2013-12-01

Tangeretin (TAN) and 5-demethyltangeretin (5DT) are two closely related polymethoxyflavones found in citrus fruits. We investigated growth inhibitory effects on three human nonsmall cell lung cancer (NSCLC) cells. Cell viability assay demonstrated that 5DT inhibited NSCLC cell growth in a time- and dose-dependent manner, and IC50 s of 5DT were 79-fold, 57-fold, and 56-fold lower than those of TAN in A549, H460, and H1299 cells, respectively. Flow cytometry analysis showed that 5DT induced extensive G2/M cell cycle arrest and apoptosis in NSCLC cells, while TAN at tenfold higher concentrations did not. The apoptosis induced by 5DT was further confirmed by activation of caspase-3 and cleavage of PARP. Moreover, 5DT dose-dependently upregulated p53 and p21(Cip1/Waf1), and downregulated Cdc-2 (Cdk-1) and cyclin B1. HPLC analysis revealed that the intracellular levels of 5DT in NSCLC cells were 2.7-4.9 fold higher than those of TAN after the cells were treated with 5DT or TAN at the same concentration. Our results demonstrated that 5DT inhibited NSCLC cell growth by inducing G2/M cell cycle arrest and apoptosis. These effects were much stronger than those produced by TAN, which is partially due to the higher intracellular uptake of 5DT than TAN. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Improving alcohol withdrawal outcomes in acute care.

PubMed

Melson, Jo; Kane, Michelle; Mooney, Ruth; Mcwilliams, James; Horton, Terry

2014-01-01

Excessive alcohol consumption is the nation's third leading cause of preventable deaths. If untreated, 6% of alcohol-dependent patients experience alcohol withdrawal, with up to 10% of those experiencing delirium tremens (DT), when they stop drinking. Without routine screening, patients often experience DT without warning. Reduce the incidence of alcohol withdrawal advancing to DT, restraint use, and transfers to the intensive care unit (ICU) in patients with DT. In October 2009, the alcohol withdrawal team instituted a care management guideline used by all disciplines, which included tools for screening, assessment, and symptom management. Data were obtained from existing datasets for three quarters before and four quarters after implementation. Follow-up data were analyzed and showed a great deal of variability in transfers to the ICU and restraint use. Percentage of patients who developed DT showed a downward trend. Incidence of alcohol withdrawal advancing to DT and, in patients with DT, restraint use and transfers to the ICU. Initial data revealed a decrease in percentage of patients with alcohol withdrawal who experienced DT (16.4%-12.9%). In patients with DT, restraint use decreased (60.4%-44.4%) and transfers to the ICU decreased (21.6%-15%). Follow-up data indicated a continued downward trend in patients with DT. Changes were not statistically significant. Restraint use and ICU transfers maintained postimplementation levels initially but returned to preimplementation levels by third quarter 2012. Early identification of patients for potential alcohol withdrawal followed by a standardized treatment protocol using symptom-triggered dosing improved alcohol withdrawal management and outcomes.

Quantification of diphtheria toxin mediated ADP-ribosylation in a solid-phase assay.

PubMed

Bachran, Christopher; Sutherland, Mark; Bachran, Diana; Fuchs, Hendrik

2007-09-01

Because of reduced vaccination programs, the number of diphtheria infections has increased in the last decade. Diphtheria toxin (DT) is expressed by Corynebacterium diphtheriae and is responsible for the lethality of diphtheria. DT inhibits cellular protein synthesis by ADP-ribosylation of the eukaryotic elongation factor 2 (eEF2). No in vitro system for the quantification of DT enzymatic activity exists. We developed a solid-phase assay for the specific detection of ADP-ribosylation by DT. Solid phase-bound his-tag eEF2 is ADP-ribosylated by toxins using biotinylated NAD(+) as substrate, and the transferred biotinylated ADP-ribose is detected by streptavidin-peroxidase. DT enzymatic activity correlated with absorbance. We measured the amount of ADP-ribosylated eEF2 after precipitation with streptavidin-Sepharose. Quantification was done after Western blotting and detection with anti-his-tag antibody using an LAS-1000 System. The assay detected enzymatically active DT at 30 ng/L, equivalent to 5 mU/L ADP-ribosylating activity. Pseudomonas exotoxin A (PE) activity was also detected at 100 ng/L. We verified the assay with chimeric toxins composed of the catalytic domain of DT or PE and a tumor-specific ligand. These chimeric toxins revealed increased signals at 1000 ng/L. Heat-inactivated DT and cholera toxin that ADP-ribosylates G-proteins did not show any signal increase. The assay may be the basis for the development of a routine diagnostic assay for the detection of DT activity and highly specific inhibitors of DT.

Drought-Tolerant Corn Hybrids Yield More in Drought-Stressed Environments with No Penalty in Non-stressed Environments

PubMed Central

Adee, Eric; Roozeboom, Kraig; Balboa, Guillermo R.; Schlegel, Alan; Ciampitti, Ignacio A.

2016-01-01

The potential benefit of drought-tolerant (DT) corn (Zea mays L.) hybrids may depend on drought intensity, duration, crop growth stage (timing), and the array of drought tolerance mechanisms present in selected hybrids. We hypothesized that corn hybrids containing DT traits would produce more consistent yields compared to non-DT hybrids in the presence of drought stress. The objective of this study was to define types of production environments where DT hybrids have a yield advantage compared to non-DT hybrids. Drought tolerant and non-DT hybrid pairs of similar maturity were planted in six site-years with different soil types, seasonal evapotranspiration (ET), and vapor pressure deficit (VPD), representing a range of macro-environments. Irrigation regimes and seeding rates were used to create several micro-environments within each macro-environment. Hybrid response to the range of macro and micro-environmental stresses were characterized in terms of water use efficiency, grain yield, and environmental index. Yield advantage of DT hybrids was positively correlated with environment ET and VPD. Drought tolerant hybrids yielded 5 to 7% more than non-DT hybrids in high and medium ET environments (>430 mm ET), corresponding to seasonal VPD greater than 1200 Pa. Environmental index analysis confirmed that DT hybrids were superior in stressful environments. Yield advantage for DT hybrids appeared as yield dropped below 10.8 Mg ha-1 and averaged as much as 0.6–1 Mg ha-1 at the low yield range. Hybrids with DT technology can offer a degree of buffering against drought stress by minimizing yield reduction, but also maintaining a comparable yield potential in high yielding environments. Further studies should focus on the physiological mechanisms presented in the commercially available corn drought tolerant hybrids. PMID:27790237

Does defibrillation testing influence outcomes after CRT-D implantation? A cause-of-death analysis from the DAI-PP study.

PubMed

Perrin, Tilman; Mechulan, Alexis; Boveda, Serge; Beganton, Frankie; Defaye, Pascal; Sadoul, Nicolas; Piot, Olivier; Klug, Didier; Gras, Daniel; Perier, Marie-Cécile; Algalarrondo, Vincent; Bordachar, Pierre; Babuty, Dominique; Fauchier, Laurent; Leclercq, Christophe; Marijon, Eloi; Deharo, Jean-Claude

2016-10-15

Little data address the usefulness of defibrillation testing in patients with prolonged QRS duration, known for more advanced myocardial disease. We aimed to compare baseline characteristics and outcomes between patients who underwent defibrillation testing (DT+) and those who did not (DT-), immediately after the implantation of a cardiac resynchronization therapy with defibrillator (CRT-D). Data from all patients with ischemic or non-ischemic cardiomyopathy implanted in primary prevention with a CRT-D in 12 French centers were considered for analysis (2002-2012). Out of the 1516 patients with DT information available, DT was performed in 958(63%) patients. Compared to DT- patients, DT+ patients presented no significant differences in terms of age (65.1±10.8 vs 64.7±10.3years, p=0.45), LVEF (25%[20.0-30.0] vs 25%[20.5-30.0], p=0.30), or etiologies of heart failure (ischemic: 49.6% vs 46.9%, p=0.32). By contrast, DT+ patients were less likely to present atrial fibrillation (25.3% vs 33.4%, p=0.001), renal insufficiency (eGFR<60ml/min in 45.3% vs 51.7%, p=0.04) and NYHA functional class≥III (68.9% vs 77.4%, p=0.0006). All of the three perioperative deaths occurred in the DT+ group and were related to DT itself. After a mean follow-up of 3.1±2.1years, the adjusted incidence of overall mortality was lower among DT+ patients (adjusted HR 0.6, 95%CI 0.4-0.7, p<0.0001). However, ICD-unresponsive sudden deaths remained very rare and no more frequently observed among DT- patients (p=0.41). In our cohort, the higher (up to 40%) mortality at midterm among DT- patients is mainly reflecting their more severe cardiac disease, rather than a higher rate of ICD-unresponsive sudden death. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The optimized V-V interval determined by interventricular conduction times versus invasive measurement by LVdP/dtMAX.

PubMed

van Gelder, Berry M; Meijer, Albert; Bracke, Frank A

2008-09-01

We compared the calculated optimal V-V interval derived from intracardiac electrograms (IEGM) with the optimized V-V interval determined by invasive measurement of LVdP/dt(MAX). Thirty-two patients with heart failure (six females, ages 68 +/- 7.8 years) had a CRT device implanted. After implantation of the atrial, right and a left ventricular lead, the optimal V-V interval was calculated using the QuickOpt formula (St. Jude Medical, Sylmar, CA, USA) applied to the respective IEGM recordings (V-V(IEGM)), and also determined by invasive measurement of LVdP/dt(MAX) (V-V(dP/dt)). The optimal V-V(IEGM) and V-V(dP/dt) intervals were 52.7 +/- 18 ms and 24.0 +/- 33 ms, respectively (P = 0.017), without correlation between the two. The baseline LVdP/dt(MAX) was 748 +/- 191 mmHg/s. The mean value of LVdP/dt(MAX) at invasive optimization was 947 +/- 198 mmHg/s, and at the calculated optimal V-V(IEGM) interval 920 +/- 191 mmHg/s (P < 0.0001). In spite of this significant difference, there was a good correlation between both methods (R = 0.991, P < 0.0001). However, a similarly good correlation existed between the maximum value of LVdP/dt(MAX) and LVdP/dt(MAX) at a fixed V-V interval of 0 ms (R = 0.993, P < 0.0001), or LVdP/dt(MAX) at a randomly selected V-V interval between 0 and +80 ms (R = 0.991, P < 0.0001). Optimizing the V-V interval with the IEGM method does not yield better hemodynamic results than simultaneous BiV pacing. Although a good correlation between LVdP/dt(MAX) determined with V-V(IEGM) and V-V(dP/dt) can be constructed, there is no correlation with the optimal settings of V-V interval in the individual patient.

Brown spider phospholipase-D containing a conservative mutation (D233E) in the catalytic site: identification and functional characterization.

PubMed

Vuitika, Larissa; Gremski, Luiza Helena; Belisário-Ferrari, Matheus Regis; Chaves-Moreira, Daniele; Ferrer, Valéria Pereira; Senff-Ribeiro, Andrea; Chaim, Olga Meiri; Veiga, Silvio Sanches

2013-11-01

Brown spider (Loxosceles genus) bites have been reported worldwide. The venom contains a complex composition of several toxins, including phospholipases-D. Native or recombinant phospholipase-D toxins induce cutaneous and systemic loxoscelism, particularly necrotic lesions, inflammatory response, renal failure, and hematological disturbances. Herein, we describe the cloning, heterologous expression and purification of a novel phospholipase-D toxin, LiRecDT7 in reference to six other previously described in phospholipase-D toxin family. The complete cDNA sequence of this novel brown spider phospholipase-D isoform was obtained and the calculated molecular mass of the predicted mature protein is 34.4 kDa. Similarity analyses revealed that LiRecDT7 is homologous to the other dermonecrotic toxin family members particularly to LiRecDT6, sharing 71% sequence identity. LiRecDT7 possesses the conserved amino acid residues involved in catalysis except for a conservative mutation (D233E) in the catalytic site. Purified LiRecDT7 was detected as a soluble 36 kDa protein using anti-whole venom and anti-LiRecDT1 sera, indicating immunological cross-reactivity and evidencing sequence-epitopes identities similar to those of other phospholipase-D family members. Also, LiRecDT7 exhibits sphingomyelinase activity in a concentration dependent-manner and induces experimental skin lesions with swelling, erythema and dermonecrosis. In addition, LiRecDT7 induced a massive inflammatory response in rabbit skin dermis, which is a hallmark of brown spider venom phospholipase-D toxins. Moreover, LiRecDT7 induced in vitro hemolysis in human erythrocytes and increased blood vessel permeability. These features suggest that this novel member of the brown spider venom phospholipase-D family, which naturally contains a mutation (D233E) in the catalytic site, could be useful for future structural and functional studies concerning loxoscelism and lipid biochemistry. 1- Novel brown spider phospholipase-D recombinant toxin contains a conservative mutation (D233E) on the catalytic site. 2-LiRecDT7 shares high identity level with isoforms of Loxosceles genus. 3-LiRecDT7 is a recombinant protein immunodetected by specific antibodies to native and recombinant phospholipase-D toxins. 4-LiRecDT7 shows sphingomyelinase-D activity in a concentration-dependent manner, but less intense than other isoforms. 5-LiRecDT7 induces dermonecrosis and inflammatory response in rabbit skin. 6-LiRecDT7 increases vascular permeability in mice. 7-LiRecDT7 triggers direct complement-independent hemolysis in erythrocytes. © 2013 Wiley Periodicals, Inc.

Branch: an interactive, web-based tool for testing hypotheses and developing predictive models.

PubMed

Gangavarapu, Karthik; Babji, Vyshakh; Meißner, Tobias; Su, Andrew I; Good, Benjamin M

2016-07-01

Branch is a web application that provides users with the ability to interact directly with large biomedical datasets. The interaction is mediated through a collaborative graphical user interface for building and evaluating decision trees. These trees can be used to compose and test sophisticated hypotheses and to develop predictive models. Decision trees are built and evaluated based on a library of imported datasets and can be stored in a collective area for sharing and re-use. Branch is hosted at http://biobranch.org/ and the open source code is available at http://bitbucket.org/sulab/biobranch/ asu@scripps.edu or bgood@scripps.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

Event Classification and Identification Based on the Characteristic Ellipsoid of Phasor Measurement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.

2011-09-23

In this paper, a method to classify and identify power system events based on the characteristic ellipsoid of phasor measurement is presented. The decision tree technique is used to perform the event classification and identification. Event types, event locations and clearance times are identified by decision trees based on the indices of the characteristic ellipsoid. A sufficiently large number of transient events were simulated on the New England 10-machine 39-bus system based on different system configurations. Transient simulations taking into account different event types, clearance times and various locations are conducted to simulate phasor measurement. Bus voltage magnitudes and recordedmore » reactive and active power flows are used to build the characteristic ellipsoid. The volume, eccentricity, center and projection of the longest axis in the parameter space coordinates of the characteristic ellipsoids are used to classify and identify events. Results demonstrate that the characteristic ellipsoid and the decision tree are capable to detect the event type, location, and clearance time with very high accuracy.« less

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

2018-03-01

A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

The economic impact of pig-associated parasitic zoonosis in Northern Lao PDR.

PubMed

Choudhury, Adnan Ali Khan; Conlan, James V; Racloz, Vanessa Nadine; Reid, Simon Andrew; Blacksell, Stuart D; Fenwick, Stanley G; Thompson, Andrew R C; Khamlome, Boualam; Vongxay, Khamphouth; Whittaker, Maxine

2013-03-01

The parasitic zoonoses human cysticercosis (Taenia solium), taeniasis (other Taenia species) and trichinellosis (Trichinella species) are endemic in the Lao People's Democratic Republic (Lao PDR). This study was designed to quantify the economic burden pig-associated zoonotic disease pose in Lao PDR. In particular, the analysis included estimation of the losses in the pork industry as well as losses due to human illness and lost productivity. A Markov-probability based decision-tree model was chosen to form the basis of the calculations to estimate the economic and public health impacts of taeniasis, trichinellosis and cysticercosis. Two different decision trees were run simultaneously on the model's human cohort. A third decision tree simulated the potential impacts on pig production. The human capital method was used to estimate productivity loss. The results found varied significantly depending on the rate of hospitalisation due to neurocysticerosis. This study is the first systematic estimate of the economic impact of pig-associated zoonotic diseases in Lao PDR that demonstrates the significance of the diseases in that country.

Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach.

PubMed

Bevilacqua, M; Ciarapica, F E; Giacchetta, G

2008-07-01

This work is an attempt to apply classification tree methods to data regarding accidents in a medium-sized refinery, so as to identify the important relationships between the variables, which can be considered as decision-making rules when adopting any measures for improvement. The results obtained using the CART (Classification And Regression Trees) method proved to be the most precise and, in general, they are encouraging concerning the use of tree diagrams as preliminary explorative techniques for the assessment of the ergonomic, management and operational parameters which influence high accident risk situations. The Occupational Injury analysis carried out in this paper was planned as a dynamic process and can be repeated systematically. The CART technique, which considers a very wide set of objective and predictive variables, shows new cause-effect correlations in occupational safety which had never been previously described, highlighting possible injury risk groups and supporting decision-making in these areas. The use of classification trees must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into traditional types of analysis.

A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

NASA Astrophysics Data System (ADS)

Książek, Judyta

2015-10-01

At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

PubMed

Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

2014-12-01

This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer.

PubMed

Miles, Kenneth A; Ganeshan, Balaji; Rodriguez-Justo, Manuel; Goh, Vicky J; Ziauddin, Zia; Engledow, Alec; Meagher, Marie; Endozo, Raymondo; Taylor, Stuart A; Halligan, Stephen; Ell, Peter J; Groves, Ashley M

2014-03-01

This study explores the potential for multifunctional imaging to provide a signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer. This prospective study approved by the institutional review board comprised 33 patients undergoing PET/CT before surgery for proven primary colorectal cancer. Tumor tissue was examined histologically for presence of the KRAS mutations and for expression of hypoxia-inducible factor-1 (HIF-1) and minichromosome maintenance protein 2 (mcm2). The following imaging parameters were derived for each tumor: (18)F-FDG uptake ((18)F-FDG maximum standardized uptake value [SUVmax]), CT texture (expressed as mean of positive pixels [MPP]), and blood flow measured by dynamic contrast-enhanced CT. A recursive decision tree was developed in which the imaging investigations were applied sequentially to identify tumors with KRAS mutations. Monte Carlo analysis provided mean values and 95% confidence intervals for sensitivity, specificity, and accuracy. The final decision tree comprised 4 decision nodes and 5 terminal nodes, 2 of which identified KRAS mutants. The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), and 90.1% (79.2%-96.0%), respectively. KRAS mutants with high (18)F-FDG SUVmax and low MPP showed greater frequency of HIF-1 expression (P = 0.032). KRAS mutants with low (18)F-FDG SUV(max), high MPP, and high blood flow expressed mcm2 (P = 0.036). Multifunctional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor (18)F-FDG uptake, CT texture, and perfusion has the potential to identify imaging signatures for colorectal cancers with KRAS mutations exhibiting hypoxic or proliferative phenotypes.

Recovery from unusual attitudes: HUD vs. back-up display in a static F/A-18 simulator.

PubMed

Huber, Samuel W

2006-04-01

Spatial disorientation (SD) remains one of the most important causes of fatal fighter aircraft accidents. The aim of this study was to give a recommendation for the use of the head-up display (HUD) or back-up attitude directional indicator (ADI) in a state of spatial disorientation based on the respective performance in an unusual attitude recovery task. Seven fighter pilots joining a conversion course to the F/A-18 participated in this study. Flight time will be presented as range (and mean in parentheses). Total military flight experience of the subjects was 835-1759 h (1412 h). Flight time on the F/A-18 was 41-123 h (70 h). The study was performed in a fixed base F/A-18D Weapons Tactics Trainer. We tested the recovery from 11 unusual attitudes and analyzed decision time (DT), total recovery time (TRT), and error rates for the HUD or the back-up ADI. We found no differences regarding either reaction times or error rates. For the HUD we found a DT (mean +/- SD) of 1.3 +/- 0.4 s, a TRT of 9.1 +/- 4.1 s, and an error rate of 29%. For the ADI the respective values were a DT of 1.4 +/- 0.4 s, a TRT of 8.3 +/- 3.8 s, and an error rate of 27%. Unusual attitude recoveries are performed equally well using the HUD or the back-up ADI. Switching from one instrument to the other during recovery should be avoided since it would probably result in a loss of time without benefit.

Dose and diagnostic image quality in digital tomosynthesis imaging of facial bones in pediatrics

NASA Astrophysics Data System (ADS)

King, J. M.; Hickling, S.; Elbakri, I. A.; Reed, M.; Wrogemann, J.

2011-03-01

The purpose of this study was to evaluate the use of digital tomosynthesis (DT) for pediatric facial bone imaging. We compared the eye lens dose and diagnostic image quality of DT facial bone exams relative to digital radiography (DR) and computed tomography (CT), and investigated whether we could modify our current DT imaging protocol to reduce patient dose while maintaining sufficient diagnostic image quality. We measured the dose to the eye lens for all three modalities using high-sensitivity thermoluminescent dosimeters (TLDs) and an anthropomorphic skull phantom. To assess the diagnostic image quality of DT compared to the corresponding DR and CT images, we performed an observer study where the visibility of anatomical structures in the DT phantom images were rated on a four-point scale. We then acquired DT images at lower doses and had radiologists indicate whether the visibility of each structure was adequate for diagnostic purposes. For typical facial bone exams, we measured eye lens doses of 0.1-0.4 mGy for DR, 0.3-3.7 mGy for DT, and 26 mGy for CT. In general, facial bone structures were visualized better with DT then DR, and the majority of structures were visualized well enough to avoid the need for CT. DT imaging provides high quality diagnostic images of the facial bones while delivering significantly lower doses to the lens of the eye compared to CT. In addition, we found that by adjusting the imaging parameters, the DT effective dose can be reduced by up to 50% while maintaining sufficient image quality.

Psychosocial Determinants of Weight Loss Among Young Adults With Overweight and Obesity: HOW DOES DRIVE FOR THINNESS AFFECT WEIGHT LOSS?

PubMed

Falck, Ryan S; Best, John R; Drenowatz, Clemens; Hand, Gregory A; Shook, Robin P; Lavie, Carl J; Blair, Steven N

2018-03-01

The ardent wish to lose weight, drive for thinness (DT), might be 1 psychosocial contributor to weight loss (WL) in adults with overweight and obesity. In examining DT as a predictor of WL, it is important to determine whether its predictive value is equal in males and females and whether it exerts its effects primarily through changes in diet or physical activity (PA). Two-hundred three men and women with overweight and obesity (body mass index >25 kg/m; aged 21-35 years; 47% female) participated in this 12-month observational study. DT score and demographic information were collected at baseline. Participants were measured at quarterly intervals for objectively measured PA, energy intake, and anthropometrics. Linear mixed regression analyses determined whether DT predicted WL over time and whether these effects were moderated by sex. Followup mediation analyses determined whether the effects of DT on WL could be explained by either changes in diet or PA. Females reported higher DT as compared with males at baseline (P < .001). We observed a significant sex × time × DT interaction on WL (P < .04), such that higher DT predicted WL in males (P < .04), but not in females (P = .54). This effect of DT on WL in overweight and obese males was mediated by changes in PA (indirect effect, -0.43; 95% CI, -1.52 to -0.05), but not changes in energy intake. Among young adults with overweight and obesity who have higher DT, PA appears to be more important to WL than caloric restriction, particularly in males.

Insurance Contract Analysis for Company Decision Support in Acquisition Management

NASA Astrophysics Data System (ADS)

Chernovita, H. P.; Manongga, D.; Iriani, A.

2017-01-01

One of company activities to retain their business is marketing the products which include in acquisition management to get new customers. Insurance contract analysis using ID3 to produce decision tree and rules to be decision support for the insurance company. The decision tree shows 13 rules that lead to contract termination claim. This could be a guide for the insurance company in acquisition management to prevent contract binding with these contract condition because it has a big chance for the customer to terminate their insurance contract before its expired date. As the result, there are several strong points that could be the determinant of contract termination such as: 1) customer age whether too young or too old, 2) long insurance period (above 10 years), 3) big insurance amount, 4) big amount of premium charges, and 5) payment method.

Comparative seed-tree and selection harvesting costs in young-growth mixed-conifer stands

Treesearch

William A. Atkinson; Dale O. Hall

1963-01-01

Little difference was found between yarding and felling costs in seed-tree and selection harvest cuts. The volume per acre logged was 23,800 board feet on the seed-tree compartments and 10,600 board feet on the selection compartments. For a comparable operation with this range of volumes, cutting method decisions should be based on factors other than logging costs....

Merger of three modeling approaches to assess potential effects of climate change on trees in the eastern United States

Treesearch

Louis R. Iverson; Anantha M. Prasad; Stephen N. Matthews; Matthew P. Peters

2010-01-01

Climate change will likely cause impacts that are species specific and significant; modeling is critical to better understand potential changes in suitable habitat. We use empirical, abundance-based habitat models utilizing decision tree-based ensemble methods to explore potential changes of 134 tree species habitats in the eastern United States (http://www.nrs.fs.fed....

Combined Knockdown of D-dopachrome Tautomerase and Migration Inhibitory Factor Inhibits the Proliferation, Migration, and Invasion in Human Cervical Cancer.

PubMed

Wang, Qingying; Wei, Yingze; Zhang, Jiawen

2017-05-01

D-dopachrome tautomerase (D-DT) is a homologue of macrophage migration inhibitory factor (MIF) with similar functions. However, the possible biological roles of D-DT in cervical cancer remain unknown so far. D-dopachrome tautomerase was assessed by immunohistochemistry in 83 cervical cancer and 31 normal cervix tissues. The stable knockdown of D-DT and MIF by lentivirus-delivered short hairpin RNA was established, and tumor growth was examined in vitro and in vivo. The effects of D-DT and MIF on the migration and invasion were further detected by wound healing assay and transwell assay. Western blot was used to explore the mechanism of D-DT and MIF in cervical cancer pathogenesis. We found that D-DT was significantly high in cervical cancer, which correlated with lymph node metastasis. The knockdown of D-DT and MIF, individually and additively, inhibited the proliferation, migration, and invasion in HeLa and SiHa cells and restrained the growth of xenograft tumor. The ablation of D-DT and MIF rescued the expression of E-cadherin and inhibited the expression of PCNA, cyclin D1, gankyrin, Sam68, and vimentin, as well as phospho-Akt and phospho-glycogen synthase kinase 3-β. The inhibition of D-DT and MIF in combination may represent a potential therapeutic strategy for cervical cancer.

A training program to improve gait while dual tasking in patients with Parkinson's disease: a pilot study.

PubMed

Yogev-Seligmann, Galit; Giladi, Nir; Brozgol, Marina; Hausdorff, Jeffrey M

2012-01-01

Impairments in the ability to perform another task while walking (ie, dual tasking [DT]) are associated with an increased risk of falling. Here we describe a program we developed specifically to improve DT performance while walking based on motor learning principles and task-specific training. We examined feasibility, potential efficacy, retention, and transfer to the performance of untrained tasks in a pilot study among 7 patients with Parkinson's disease (PD). Seven patients (Hoehn and Yahr stage, 2.1±0.2) were evaluated before, after, and 1 month after 4 weeks of DT training. Gait speed and gait variability were measured during usual walking and during 4 DT conditions. The 4-week program of one-on-one training included walking while performing several distinct cognitive tasks. Gait speed and gait variability during DT significantly improved. Improvements were also seen in the DT conditions that were not specifically trained and were retained 1 month after training. These initial findings support the feasibility of applying a task-specific DT gait training program for patients with PD and suggest that it positively affects DT gait, even in untrained tasks. The present results are also consistent with the possibility that DT gait training enhances divided attention abilities during walking. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

The role of gender in the association between personality and task priority in older adults' dual-tasking while walking.

PubMed

Agmon, Maayan; Armon, Galit; Denesh, Shani; Doumas, Mihalis

2018-01-02

Falls are a major problem for older adults. Many falls occur when a person's attention is divided between two tasks, such as a dual task (DT) involving walking. Most recently, the role of personality in walking performance was addressed; however, its association with DT performance remains to be determined. This cross-sectional study of 73 older, community-dwelling adults explores the association between personality and DT walking and the role of gender in this relationship. Personality was evaluated using the five-factor model. Single-task (ST) and DT assessment of walking-cognitive DT performance comprised a 1-min walking task and an arithmetic task performed separately (ST) and concurrently (DT). Dual-task costs (DTCs), reflecting the proportional difference between ST and DT performance, were also calculated. Gender plays a role in the relationship between personality and DT. Extraversion was negatively associated with DTC-motor for men (ΔR 2  = 0.06, p < 0.05). Conscientiousness was positively associated with DTC-cognition for women (ΔR 2  = 0.08, p < 0.01). These findings may lead to effective personality-based early detection and intervention for fall prevention.

Indications of flow near maximum compression in layered deuterium-tritium implosions at the National Ignition Facility

DOE PAGES

Gatu Johnson, M.; Knauer, J. P.; Cerjan, C. J.; ...

2016-08-15

Here, an accurate understanding of burn dynamics in implosions of cryogenically layered deuterium (D) and tritium (T) filled capsules, obtained partly through precision diagnosis of these experiments, is essential for assessing the impediments to achieving ignition at the National Ignition Facility. We present measurements of neutrons from such implosions. The apparent ion temperatures T ion are inferred from the variance of the primary neutron spectrum. Consistently higher DT than DD T ion are observed and the difference is seen to increase with increasing apparent DT T ion. The line-of-sight rms variations of both DD and DT T ion are small,more » ~150eV, indicating an isotropic source. The DD neutron yields are consistently high relative to the DT neutron yields given the observed T ion. Spatial and temporal variations of the DT temperature and density, DD-DT differential attenuation in the surrounding DT fuel, and fluid motion variations contribute to a DT Tion greater than the DD T ion, but are in a one-dimensional model insufficient to explain the data. We hypothesize that in a three-dimensional interpretation, these effects combined could explain the results.« less

Indications of flow near maximum compression in layered deuterium-tritium implosions at the National Ignition Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gatu Johnson, M.; Knauer, J. P.; Cerjan, C. J.

Here, an accurate understanding of burn dynamics in implosions of cryogenically layered deuterium (D) and tritium (T) filled capsules, obtained partly through precision diagnosis of these experiments, is essential for assessing the impediments to achieving ignition at the National Ignition Facility. We present measurements of neutrons from such implosions. The apparent ion temperatures T ion are inferred from the variance of the primary neutron spectrum. Consistently higher DT than DD T ion are observed and the difference is seen to increase with increasing apparent DT T ion. The line-of-sight rms variations of both DD and DT T ion are small,more » ~150eV, indicating an isotropic source. The DD neutron yields are consistently high relative to the DT neutron yields given the observed T ion. Spatial and temporal variations of the DT temperature and density, DD-DT differential attenuation in the surrounding DT fuel, and fluid motion variations contribute to a DT Tion greater than the DD T ion, but are in a one-dimensional model insufficient to explain the data. We hypothesize that in a three-dimensional interpretation, these effects combined could explain the results.« less

Divide and Conquer: A Valid Approach for Risk Assessment and Decision Making under Uncertainty for Groundwater-Related Diseases

NASA Astrophysics Data System (ADS)

Sanchez-Vila, X.; de Barros, F.; Bolster, D.; Nowak, W.

2010-12-01

Assessing the potential risk of hydro(geo)logical supply systems to human population is an interdisciplinary field. It relies on the expertise in fields as distant as hydrogeology, medicine, or anthropology, and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties in hydrological, physiological and human behavioral parameters. We propose the use of fault trees to address the task of probabilistic risk analysis (PRA) and to support related management decisions. Fault trees allow decomposing the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural “Divide and Conquer” approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance and stage of analysis. The separation in modules allows for a true inter- and multi-disciplinary approach. This presentation highlights the three novel features of our work: (1) we define failure in terms of risk being above a threshold value, whereas previous studies used auxiliary events such as exceedance of critical concentration levels, (2) we plot an integrated fault tree that handles uncertainty in both hydrological and health components in a unified way, and (3) we introduce a new form of stochastic fault tree that allows to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Single- and Dual-Task Balance Training Are Equally Effective in Youth

PubMed Central

Lüder, Benjamin; Kiss, Rainer; Granacher, Urs

2018-01-01

Due to maturation of the postural control system and secular declines in motor performance, adolescents experience deficits in postural control during standing and walking while concurrently performing cognitive interference tasks. Thus, adequately designed balance training programs may help to counteract these deficits. While the general effectiveness of youth balance training is well-documented, there is hardly any information available on the specific effects of single-task (ST) versus dual-task (DT) balance training. Therefore, the objectives of this study were (i) to examine static/dynamic balance performance under ST and DT conditions in adolescents and (ii) to study the effects of ST versus DT balance training on static/dynamic balance under ST and DT conditions in adolescents. Twenty-eight healthy girls and boys aged 12–13 years were randomly assigned to either 8 weeks of ST or DT balance training. Before and after training, postural sway and spatio-temporal gait parameters were registered under ST (standing/walking only) and DT conditions (standing/walking while concurrently performing an arithmetic task). At baseline, significantly slower gait speed (p < 0.001, d = 5.1), shorter stride length (p < 0.001, d = 4.8), and longer stride time (p < 0.001, d = 3.8) were found for DT compared to ST walking but not standing. Training resulted in significant pre–post decreases in DT costs for gait velocity (p < 0.001, d = 3.1), stride length (-45%, p < 0.001, d = 2.4), and stride time (-44%, p < 0.01, d = 1.9). Training did not induce any significant changes (p > 0.05, d = 0–0.1) in DT costs for all parameters of secondary task performance during standing and walking. Training produced significant pre–post increases (p = 0.001; d = 1.47) in secondary task performance while sitting. The observed increase was significantly greater for the ST training group (p = 0.04; d = 0.81). For standing, no significant changes were found over time irrespective of the experimental group. We conclude that adolescents showed impaired DT compared to ST walking but not standing. ST and DT balance training resulted in significant and similar changes in DT costs during walking. Thus, there appears to be no preference for either ST or DT balance training in adolescents. PMID:29928248

Single- and Dual-Task Balance Training Are Equally Effective in Youth.

PubMed

Lüder, Benjamin; Kiss, Rainer; Granacher, Urs

2018-01-01

Due to maturation of the postural control system and secular declines in motor performance, adolescents experience deficits in postural control during standing and walking while concurrently performing cognitive interference tasks. Thus, adequately designed balance training programs may help to counteract these deficits. While the general effectiveness of youth balance training is well-documented, there is hardly any information available on the specific effects of single-task (ST) versus dual-task (DT) balance training. Therefore, the objectives of this study were (i) to examine static/dynamic balance performance under ST and DT conditions in adolescents and (ii) to study the effects of ST versus DT balance training on static/dynamic balance under ST and DT conditions in adolescents. Twenty-eight healthy girls and boys aged 12-13 years were randomly assigned to either 8 weeks of ST or DT balance training. Before and after training, postural sway and spatio-temporal gait parameters were registered under ST (standing/walking only) and DT conditions (standing/walking while concurrently performing an arithmetic task). At baseline, significantly slower gait speed ( p < 0.001, d = 5.1), shorter stride length ( p < 0.001, d = 4.8), and longer stride time ( p < 0.001, d = 3.8) were found for DT compared to ST walking but not standing. Training resulted in significant pre-post decreases in DT costs for gait velocity ( p < 0.001, d = 3.1), stride length (-45%, p < 0.001, d = 2.4), and stride time (-44%, p < 0.01, d = 1.9). Training did not induce any significant changes ( p > 0.05, d = 0-0.1) in DT costs for all parameters of secondary task performance during standing and walking. Training produced significant pre-post increases ( p = 0.001; d = 1.47) in secondary task performance while sitting. The observed increase was significantly greater for the ST training group ( p = 0.04; d = 0.81). For standing, no significant changes were found over time irrespective of the experimental group. We conclude that adolescents showed impaired DT compared to ST walking but not standing. ST and DT balance training resulted in significant and similar changes in DT costs during walking. Thus, there appears to be no preference for either ST or DT balance training in adolescents.

Aircraft Hydraulic Systems Dynamic Analysis. Volume II. Transient Analysis (HYTRAN) Computer Program Technical Description

DTIC Science & Technology

1977-02-01

Corporation, under contract F3615-74-C- 2016 . A The effort was sponsored by the Air Force Aero Propulsion Laboratory, Air Force Systems Command, Wright...2100 DT(IVOLO) = D(MIVOLO) D’T(PG) IY DT(PRESIPS) 2100 I)T(KBULK) = BULK( KTEMPI (IND) )*I)T(NDELT)/D(MAVOLO) DT(NVZ) = DT(NZ)+. 0I/DOT (KBULK) 6.71-4

The third-stimulus temporal discrimination threshold: focusing on the temporal processing of sensory input within primary somatosensory cortex.

PubMed

Leodori, Giorgio; Formica, Alessandra; Zhu, Xiaoying; Conte, Antonella; Belvisi, Daniele; Cruccu, Giorgio; Hallett, Mark; Berardelli, Alfredo

2017-10-01

The somatosensory temporal discrimination threshold (STDT) has been used in recent years to investigate time processing of sensory information, but little is known about the physiological correlates of somatosensory temporal discrimination. The objective of this study was to investigate whether the time interval required to discriminate between two stimuli varies according to the number of stimuli in the task. We used the third-stimulus temporal discrimination threshold (ThirdDT), defined as the shortest time interval at which an individual distinguishes a third stimulus following a pair of stimuli delivered at the STDT. The STDT and ThirdDT were assessed in 31 healthy subjects. In a subgroup of 10 subjects, we evaluated the effects of the stimuli intensity on the ThirdDT. In a subgroup of 16 subjects, we evaluated the effects of S1 continuous theta-burst stimulation (S1-cTBS) on the STDT and ThirdDT. Results show that ThirdDT is shorter than STDT. We found a positive correlation between STDT and ThirdDT values. As long as the stimulus intensity was within the perceivable and painless range, it did not affect ThirdDT values. S1-cTBS significantly affected both STDT and ThirdDT, although the latter was affected to a greater extent and for a longer period of time. We conclude that the interval needed to discriminate between time-separated tactile stimuli is related to the number of stimuli used in the task. STDT and ThirdDT are encoded in S1, probably by a shared tactile temporal encoding mechanism whose performance rapidly changes during the perception process. ThirdDT is a new method to measure somatosensory temporal discrimination. NEW & NOTEWORTHY To investigate whether the time interval required to discriminate between stimuli varies according to changes in the stimulation pattern, we used the third-stimulus temporal discrimination threshold (ThirdDT). We found that the somatosensory temporal discrimination acuity varies according to the number of stimuli in the task. The ThirdDT is a new method to measure somatosensory temporal discrimination and a possible index of inhibitory activity at the S1 level. Copyright © 2017 the American Physiological Society.

Tree-to-tree variation in seed size and its consequences for seed dispersal versus predation by rodents.

PubMed

Wang, Bo; Ives, Anthony R

2017-03-01

Individual variation in seed size and seed production is high in many plant species. How does this variation affect seed-dispersing animals and, in turn, the fitness of individual plants? In this study, we first surveyed intraspecific variation in seed mass and production in a population of a Chinese white pine, Pinus armandii. For 134 target trees investigated in 2012, there was very high variation in seed size, with mean seed mass varying among trees almost tenfold, from 0.038 to 0.361 g. Furthermore, 30 of the 134 trees produced seeds 2 years later, and for these individuals there was a correlation in seed mass of 0.59 between years, implying consistent differences among individuals. For a subset of 67 trees, we monitored the foraging preferences of scatter-hoarding rodents on a total of 15,301 seeds: 8380 were ignored, 3184 were eaten in situ, 2651 were eaten after being cached, and 395 were successfully dispersed (cached and left intact). At the scale of individual seeds, seed mass affected almost every decision that rodents made to eat, remove, and cache individual seeds. At the level of individual trees, larger seeds had increased probabilities of both predation and successful dispersal: the effects of mean seed size on costs (predation) and benefits (caching) balanced out. Thus, despite seed size affecting rodent decisions, variation among trees in dispersal success associated with mean seed size was small once seeds were harvested. This might explain, at least in part, the maintenance of high variation in mean seed mass among tree individuals.

Savant memory for digits in a case of synaesthesia and Asperger syndrome is related to hyperactivity in the lateral prefrontal cortex.

PubMed

Bor, Daniel; Billington, Jac; Baron-Cohen, Simon

2007-10-01

SINGLE CASE: DT is a savant with exceptional abilities in numerical memory and mathematical calculations. DT also has an elaborate form of synaesthesia for visually presented digits. Further more, DT also has Asperger syndrome (AS). We carried out two preliminary investigations to establish whether these conditions may contribute to his savant abilities. In an fMRI digit span study, DT showed hyperactivity in lateral prefrontal cortex when encoding digits, compared with controls. In addition, while controls showed raised lateral prefrontal activation in response to structured (compared to unstructured) sequences of digits, DT's neural activity did not differ between these two conditions. In addition, controls showed a significant performance advantage for structured, compared with unstructured sequences whereas no such pattern was found for DT. We suggest that this performance pattern reflects that DT focuses less on external mathematical structure, since for him all digit sequences have internal structure linked to his synaesthesia. Finally, DT did not activate extra-striate regions normally associated with synaesthesia, suggesting that he has an unusual and more abstract and conceptual form of synaesthesia. This appears to generate structured, highly-chunked content that enhances encoding of digits and aids both recall and calculation. People with AS preferentially attend to local features of stimuli. To test this in DT, we administered the Navon task. Relative to controls, DT was faster at finding a target at the local level, and was less distracted by interference from the global level. The propensity to focus on local detail, in concert with a form of synaesthesia that provides structure to all digits, may account for DT's exceptional numerical memory and calculation ability. This neural and cognitive pattern needs to be tested in a series of similar cases, and with more constrained control groups, to confirm the significance of this association.

Cost-Effectiveness of Ventricular Assist Device Destination Therapy for Advanced Heart Failure in Duchenne Muscular Dystrophy.

PubMed

Magnetta, Defne A; Kang, JaHyun; Wearden, Peter D; Smith, Kenneth J; Feingold, Brian

2018-05-17

Destination ventricular assist device therapy (DT-VAD) is well accepted in select adults with medically refractory heart failure (HF) who are not transplant candidates; however, its use in younger patients with progressive diseases is unclear. We sought to evaluate the cost-effectiveness of DT-VAD in Duchenne muscular dystrophy (DMD) patients with advanced HF. We created a Markov-state transition model (5-year horizon) to compare survival, costs, and quality of life (QOL) between medical management and DT-VAD in DMD with advanced HF. Model input parameters were derived from the literature. We used sensitivity analyses to explore uncertainty around model assumptions. DT-VAD had higher costs ($435,602 vs. $125,696), survival (3.13 vs. 0.60 years), and quality-adjusted survival (1.99 vs. 0.26 years) than medical management. The incremental cost-effectiveness ratio (ICER) for DT-VAD was $179,086 per quality-adjusted life year (QALY). In sensitivity analyses that were widely varied to account for uncertainty in model assumptions, the DT-VAD strategy generally remained more costly and effective than medical management. Only when VAD implantation costs were <$113,142 did the DT-VAD strategy fall below the $100,000/QALY willingness-to-pay threshold commonly considered to be "cost-effective." In this exploratory analysis, DT-VAD for patients with DMD and advanced HF exceeded societal expectations for cost-effectiveness but had an ICER similar to the accepted practice of DT-VAD in adult HF patients. While more experience and research in this population is needed, our analysis suggests that DT-VAD for advanced HF in DMD should not be dismissed solely based on cost.

How does the Distress Thermometer compare to the Hospital Anxiety and Depression Scale for detecting possible cases of psychological morbidity among cancer survivors?

PubMed

Boyes, Allison; D'Este, Catherine; Carey, Mariko; Lecathelinais, Christophe; Girgis, Afaf

2013-01-01

Use of the Distress Thermometer (DT) as a screening tool is increasing across the cancer trajectory. This study examined the accuracy and optimal cut-off score of the DT compared to the Hospital Anxiety and Depression Scale (HADS) for detecting possible cases of psychological morbidity among adults in early survivorship. This study is a cross-sectional survey of 1,323 adult cancer survivors recruited from two state-based cancer registries in Australia. Participants completed the DT and the HADS at 6 months post-diagnosis. Compared to the HADS subscale threshold ≥8, the DT performed well in discriminating between cases and non-cases of anxiety, depression and comorbid anxiety-depression with an area under the curve of 0.85, 0.84 and 0.87, respectively. A DT cut-off score of ≥2 was best for clinical use (sensitivity, 87-95 %; specificity, 60-68 %), ≥4 was best for research use (sensitivity, 67-82 %; specificity, 81-88 %) and ≥3 was the best balance between sensitivity (77-88 %) and specificity (72-79 %) for detecting cases of anxiety, depression and comorbid anxiety-depression. The DT demonstrated a high level of precision in identifying non-cases of psychological morbidity at all possible thresholds (negative predictive value, 77-99 %). The recommended DT cut-off score of ≥4 was not supported for universal use among recent cancer survivors. The optimal DT threshold depends upon whether the tool is being used in the clinical or research setting. The DT may best serve to initially identify non-cases as part of a two-stage screening process. The performance of the DT against 'gold standard' clinical interview should be evaluated with cancer survivors.

Digital tomosynthesis for evaluating metastatic lung nodules: nodule visibility, learning curves, and reading times.

PubMed

Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyungjin; Song, Yong Sub; Hwang, Eui Jin

2015-01-01

To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, ≤ 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

Diagnosis of response and non-response to dry eye treatment using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Vidya, S.; Yeo, Sharon; Too, Cheah Loon; Lim, Wei Jie Eugene; Chua, Kuang Chua; Tong, Louis

2014-11-01

The dry eye treatment outcome depends on the assessment of clinical relevance of the treatment effect. The potential approach to assess the clinical relevance of the treatment is to identify the symptoms responders and non-responders to the given treatments using the responder analysis. In our work, we have performed the responder analysis to assess the clinical relevance effect of the dry eye treatments namely, hot towel, EyeGiene®, and Blephasteam® twice daily and 12 min session of Lipiflow®. Thermography is performed at week 0 (baseline), at weeks 4 and 12 after treatment. The clinical parameters such as, change in the clinical irritations scores, tear break up time (TBUT), corneal staining and Schirmer's symptoms tests values are used to obtain the responders and non-responders groups. We have obtained the infrared thermography images of dry eye symptoms responders and non-responders to the three types of warming treatments. The energy, kurtosis, skewness, mean, standard deviation, and various entropies namely Shannon, Renyi and Kapoor are extracted from responders and non-responders thermograms. The extracted features are ranked based on t-values. These ranked features are fed to the various classifiers to get the highest performance using minimum features. We have used decision tree (DT), K nearest neighbour (KNN), Naves Bayesian (NB) and support vector machine (SVM) to classify the features into responder and non-responder classes. We have obtained an average accuracy of 99.88%, sensitivity of 99.7% and specificity of 100% using KNN classifier using ten-fold cross validation.

Forestry 101.

ERIC Educational Resources Information Center

Markham, Mary T.

2000-01-01

Introduces a unit on forest management in which students manage the school forest. Involves students in tree identification, determining the size or volume and height of trees, and evaluation of the forest for management decisions. Integrates mathematics, writing, and social studies with plant classification, plant reproduction, and the use of…

Using dolls for therapeutic purposes: A study on nursing home residents with severe dementia.

PubMed

Cantarella, A; Borella, E; Faggian, S; Navuzzi, A; De Beni, R

2018-04-19

Among the psychosocial interventions intended to reduce the behavioral and psychological symptoms of dementia (BPSD), doll therapy (DT) is increasingly used in clinical practice. Few studies on DT have been based on empirical data obtained with an adequate procedure; however, none have assessed its efficacy using an active control group, and the scales used to assess changes in BPSD are usually unreliable. The aim of the present study was to measure the impact of DT on people with severe dementia with a reliable, commonly used scale for assessing their BPSD, and the related distress in formal caregivers. Effects of DT on the former's everyday abilities (ie, eating behavior) were also examined. Twenty-nine nursing home residents aged from 76 to 96 years old, with severe dementia (Alzheimer's or vascular dementia), took part in the experiment. They were randomly assigned to an experimental group that used dolls or an active control group that used hand warmers with sensory characteristics equivalent to the dolls. Benefits of DT on BPSD and related formal caregiver distress were examined with the Neuropsychiatric Inventory. The effects of DT on eating behavior were examined with the Eating Behavior Scale. Only the DT group showed a reduction in BPSD scores and related caregiver distress. DT did not benefit eating behavior, however. This study suggests that DT is a promising approach for reducing BPSD in people with dementia, supporting evidence emerging from previous anecdotal studies. Copyright © 2018 John Wiley & Sons, Ltd.

Decision and Game Theory for Security

NASA Astrophysics Data System (ADS)

Alpcan, Tansu; Buttyán, Levente; Baras, John S.

Attack--defense trees are used to describe security weaknesses of a system and possible countermeasures. In this paper, the connection between attack--defense trees and game theory is made explicit. We show that attack--defense trees and binary zero-sum two-player extensive form games have equivalent expressive power when considering satisfiability, in the sense that they can be converted into each other while preserving their outcome and their internal structure.

Interactions between factors related to the decision of sex offenders to confess during police interrogation: a classification-tree approach.

PubMed

Beauregard, Eric; Deslauriers-Varin, Nadine; St-Yves, Michel

2010-09-01

Most studies of confessions have looked at the influence of individual factors, neglecting the potential interactions between these factors and their impact on the decision to confess or not during an interrogation. Classification and regression tree analyses conducted on a sample of 624 convicted sex offenders showed that certain factors related to the offenders (e.g., personality, criminal career), victims (e.g., sex, relationship to offender), and case (e.g., time of day of the crime) were related to the decision to confess or not during the police interrogation. Several interactions were also observed between these factors. Results will be discussed in light of previous findings and interrogation strategies for sex offenders.

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework.

PubMed

Angelis, Aris; Kanavos, Panos

2017-09-01

Escalating drug prices have catalysed the generation of numerous "value frameworks" with the aim of informing payers, clinicians and patients on the assessment and appraisal process of new medicines for the purpose of coverage and treatment selection decisions. Although this is an important step towards a more inclusive Value Based Assessment (VBA) approach, aspects of these frameworks are based on weak methodologies and could potentially result in misleading recommendations or decisions. In this paper, a Multiple Criteria Decision Analysis (MCDA) methodological process, based on Multi Attribute Value Theory (MAVT), is adopted for building a multi-criteria evaluation model. A five-stage model-building process is followed, using a top-down "value-focused thinking" approach, involving literature reviews and expert consultations. A generic value tree is structured capturing decision-makers' concerns for assessing the value of new medicines in the context of Health Technology Assessment (HTA) and in alignment with decision theory. The resulting value tree (Advance Value Tree) consists of three levels of criteria (top level criteria clusters, mid-level criteria, bottom level sub-criteria or attributes) relating to five key domains that can be explicitly measured and assessed: (a) burden of disease, (b) therapeutic impact, (c) safety profile (d) innovation level and (e) socioeconomic impact. A number of MAVT modelling techniques are introduced for operationalising (i.e. estimating) the model, for scoring the alternative treatment options, assigning relative weights of importance to the criteria, and combining scores and weights. Overall, the combination of these MCDA modelling techniques for the elicitation and construction of value preferences across the generic value tree provides a new value framework (Advance Value Framework) enabling the comprehensive measurement of value in a structured and transparent way. Given its flexibility to meet diverse requirements and become readily adaptable across different settings, the Advance Value Framework could be offered as a decision-support tool for evaluators and payers to aid coverage and reimbursement of new medicines. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Prospective Examination of the Relations between Emotional Abuse and Anxiety: Moderation by Distress Tolerance

PubMed Central

Banducci, Anne N.; Lejuez, C.W.; Dougherty, Lea R.; MacPherson, Laura

2016-01-01

Objective Anxiety, the most common and impairing psychological problem experienced by youth, is associated with numerous individual and environmental factors. Two such factors include childhood emotional abuse (CEA) and low distress tolerance (DT). The current study aimed to understand how CEA and low DT impacted anxiety symptoms measured annually across five years among a community sample of youth. We hypothesized DT would moderate the relationship between CEA and anxiety, such that youth with higher levels of CEA and lower levels of DT would have elevated anxiety over time. Method Community youth (N = 244) were annually assessed across five years using the Revised Child Anxiety and Depression Scale, Childhood Trauma Questionnaire, and Behavioral Indicator of Resiliency to Distress. Results Higher CEA at baseline was associated with higher anxiety at baseline, higher anxiety at each annual assessment, and with greater overall decreases in anxiety over time. Lower DT was associated with higher anxiety at baseline, but did not predict changes in anxiety over time. Baseline DT significantly moderated the relationship between baseline CEA and anxiety, such that youth with both higher CEA and lower DT had the highest anxiety at each annual assessment. Conclusions Youth with lower DT and higher CEA scores had the highest level of anxiety symptoms across time. PMID:27501698

A Prospective Examination of the Relations Between Emotional Abuse and Anxiety: Moderation by Distress Tolerance.

PubMed

Banducci, Anne N; Lejuez, C W; Dougherty, Lea R; MacPherson, Laura

2017-01-01

Anxiety, the most common and impairing psychological problem experienced by youth, is associated with numerous individual and environmental factors. Two such factors include childhood emotional abuse (CEA) and low distress tolerance (DT). The current study aimed to understand how CEA and low DT impacted anxiety symptoms measured annually across 5 years among a community sample of youth. We hypothesized DT would moderate the relationship between CEA and anxiety, such that youth with higher levels of CEA and lower levels of DT would have elevated anxiety over time. Community youth (N = 244) were annually assessed across 5 years using the Revised Child Anxiety and Depression Scale, Childhood Trauma Questionnaire, and Behavioral Indicator of Resiliency to Distress. Higher CEA at baseline was associated with higher anxiety at baseline, higher anxiety at each annual assessment, and with greater overall decreases in anxiety over time. Lower DT was associated with higher anxiety at baseline, but did not predict changes in anxiety over time. Baseline DT significantly moderated the relationship between baseline CEA and anxiety, such that youth with both higher CEA and lower DT had the highest anxiety at each annual assessment. Youth with lower DT and higher CEA scores had the highest level of anxiety symptoms across time.

Dose assessment of digital tomosynthesis in pediatric imaging

NASA Astrophysics Data System (ADS)

Gislason, Amber; Elbakri, Idris A.; Reed, Martin

2009-02-01

We investigated the potential for digital tomosynthesis (DT) to reduce pediatric x-ray dose while maintaining image quality. We utilized the DT feature (VolumeRadTM) on the GE DefiniumTM 8000 flat panel system installed in the Winnipeg Children's Hospital. Facial bones, cervical spine, thoracic spine, and knee of children aged 5, 10, and 15 years were represented by acrylic phantoms for DT dose measurements. Effective dose was estimated for DT and for corresponding digital radiography (DR) and computed tomography (CT) patient image sets. Anthropomorphic phantoms of selected body parts were imaged by DR, DT, and CT. Pediatric radiologists rated visualization of selected anatomic features in these images. Dose and image quality comparisons between DR, DT, and CT determined the usefulness of tomosynthesis for pediatric imaging. CT effective dose was highest; total DR effective dose was not always lowest - depending how many projections were in the DR image set. For the cervical spine, DT dose was close to and occasionally lower than DR dose. Expert radiologists rated visibility of the central facial complex in a skull phantom as better than DR and comparable to CT. Digital tomosynthesis has a significantly lower dose than CT. This study has demonstrated DT shows promise to replace CT for some facial bones and spinal diagnoses. Other clinical applications will be evaluated in the future.

Study of DT-diaphorase in pigment-producing cells.

PubMed

Smit, N P; Hoogduijn, M J; Riley, P A; Pavel, S

1999-11-01

DT-diaphorase is an FAD-containing enzyme capable of a two-electron reduction of ortho- and paraquinones. Nicotinamide coenzymes (NADH + H+ and NADPH + H+) serve as hydrogen sources in these reactions. The role of DT-diaphorase has been thoroughly investigated in situations when the enzyme is able to reduce exogenous and endogenous quinones, hence protecting the cells against these reactive intermediates. The enzyme has also been studied in connection with its ability to activate some quinoid cytostatics. It is surprising that DT-diaphorase has never been investigated in pigment-producing cells that are known to generate considerable amounts of ortho-quinones. Using a spectrophotometric method we could readily measure the activity of DT-diaphorase in epidermis and various cultured pigment cells. The melanocytes isolated from dark skin showed generally higher DT-diaphorase activity than those from fair skin samples. Also, darkly pigmented congenital naevus cells exhibited higher activity of this enzyme. The most striking was the high DT-diaphorase activity in melanoma cell cultures. In these cells DT-diaphorase activity could be induced by incubation of the cells with 4-hydroxyanisole. A similar effect was seen when a catechol-O-methyltransferase (COMT) inhibitor (3-(3,4-dihydroxy-5-nitrobenzylidene)-2,4-pentanedione (OR-462) was utilised. The induction was inhibited by cyclohexidine.

Integrated Design Analysis and Optimisation of Aircraft Structures (L’Analyse pour la Conception Integree et l’Optimisation des Structures d’Aeronefs)

DTIC Science & Technology

1992-02-01

Division (Code RM) ONERA Office of Aeronautics & Space Technology 29 ave de la Division Leclerc NASA Hq 92320 Chfitillon Washington DC 20546 France United...Vector of thickness variables. V’ = [ t2 ........ tN Vector of thickness changes. AV ’= [rt, 5t2 ......... tNJ TI 7-9 Vector of strain derivatives. F...ds, ds, I d, 1i’,= dt, dr2 ........ dt--N Vector of buckling derivatives. dX d). , dt1 dt2 dtN Then 5F= Vs’i . AV and SX V,’. AV The linearised

Laser fusion neutron source employing compression with short pulse lasers

DOEpatents

Sefcik, Joseph A; Wilks, Scott C

2013-11-05

A method and system for achieving fusion is provided. The method includes providing laser source that generates a laser beam and a target that includes a capsule embedded in the target and filled with DT gas. The laser beam is directed at the target. The laser beam helps create an electron beam within the target. The electron beam heats the capsule, the DT gas, and the area surrounding the capsule. At a certain point equilibrium is reached. At the equilibrium point, the capsule implodes and generates enough pressure on the DT gas to ignite the DT gas and fuse the DT gas nuclei.

Office of Legacy Management Decision Tree for Solar Photovoltaic Projects - 13317

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elmer, John; Butherus, Michael; Barr, Deborah L.

2013-07-01

To support consideration of renewable energy power development as a land reuse option, the DOE Office of Legacy Management (LM) and the National Renewable Energy Laboratory (NREL) established a partnership to conduct an assessment of wind and solar renewable energy resources on LM lands. From a solar capacity perspective, the larger sites in the western United States present opportunities for constructing solar photovoltaic (PV) projects. A detailed analysis and preliminary plan was developed for three large sites in New Mexico, assessing the costs, the conceptual layout of a PV system, and the electric utility interconnection process. As a result ofmore » the study, a 1,214-hectare (3,000-acre) site near Grants, New Mexico, was chosen for further study. The state incentives, utility connection process, and transmission line capacity were key factors in assessing the feasibility of the project. LM's Durango, Colorado, Disposal Site was also chosen for consideration because the uranium mill tailings disposal cell is on a hillside facing south, transmission lines cross the property, and the community was very supportive of the project. LM worked with the regulators to demonstrate that the disposal cell's long-term performance would not be impacted by the installation of a PV solar system. A number of LM-unique issues were resolved in making the site available for a private party to lease a portion of the site for a solar PV project. A lease was awarded in September 2012. Using a solar decision tree that was developed and launched by the EPA and NREL, LM has modified and expanded the decision tree structure to address the unique aspects and challenges faced by LM on its multiple sites. The LM solar decision tree covers factors such as land ownership, usable acreage, financial viability of the project, stakeholder involvement, and transmission line capacity. As additional sites are transferred to LM in the future, the decision tree will assist in determining whether a solar PV project is feasible on the new sites. (authors)« less

Influence of diagnosis threat and illness cognitions on the cognitive performance of people with acquired brain injury.

PubMed

Fresson, Megan; Dardenne, Benoit; Meulemans, Thierry

2018-02-27

Illness cognitions - cognitive representations of illness - have been found to influence health outcomes in chronic diseases: more adaptive illness cognitions generally lead to better outcomes. Concomitantly, diagnosis threat (DT) is a phenomenon whereby participants with acquired brain injury (ABI) underperform on neuropsychological tasks due to stereotype activation. This randomised study examined the impact of illness cognitions and DT on cognitive performance. People with ABI completed the Illness Cognitions Questionnaire and were then exposed to either a DT condition or a reduced DT condition (in which stereotype cues were reduced). They then completed memory and attentional tasks. Control participants performed only the tasks under one of the two conditions. Under the reduced DT condition, higher adaptive illness cognitions were associated with better memory and attentional performance. However, the DT condition diminished memory (but not attentional) performance in participants with a high level of adaptive illness cognitions, often leading to performance at the pathological level. This study confirms the detrimental impact of DT in people with ABI and highlights the necessity for clinicians to consider psychosocial influences when assessing and treating this population.

Learning classification trees

NASA Technical Reports Server (NTRS)

Buntine, Wray

1991-01-01

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Decision tree analysis as a supplementary tool to enhance histomorphological differentiation when distinguishing human from non-human cranial bone in both burnt and unburnt states: A feasibility study.

PubMed

Simmons, T; Goodburn, B; Singhrao, S K

2016-01-01

This feasibility study was undertaken to describe and record the histological characteristics of burnt and unburnt cranial bone fragments from human and non-human bones. Reference series of fully mineralized, transverse sections of cranial bone, from all variables and specimen states, were prepared by manual cutting and semi-automated grinding and polishing methods. A photomicrograph catalogue reflecting differences in burnt and unburnt bone from human and non-humans was recorded and qualitative analysis was performed using an established classification system based on primary bone characteristics. The histomorphology associated with human and non-human samples was, for the main part, preserved following burning at high temperature. Clearly, fibro-lamellar complex tissue subtypes, such as plexiform or laminar primary bone, were only present in non-human bones. A decision tree analysis based on histological features provided a definitive identification key for distinguishing human from non-human bone, with an accuracy of 100%. The decision tree for samples where burning was unknown was 96% accurate, and multi-step classification to taxon was possible with 100% accuracy. The results of this feasibility study strongly suggest that histology remains a viable alternative technique if fragments of cranial bone require forensic examination in both burnt and unburnt states. The decision tree analysis may provide an additional but vital tool to enhance data interpretation. Further studies are needed to assess variation in histomorphology taking into account other cranial bones, ontogeny, species and burning conditions. © The Author(s) 2015.

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

PubMed

Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

2016-11-01

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

PubMed

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

2012-05-30

The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. Copyright © 2012 Elsevier B.V. All rights reserved.

Three screening methods for cognitive dysfunction using the Mini-Mental State Examination and Korean Dementia Screening Questionnaire.

PubMed

Choi, Seong Hye; Park, Moon Ho

2016-02-01

To screen for and determine cognitive dysfunction, cognitive tests and/or informant reports are commonly used. However, these cognitive tests and informant reports are not always available. The present study investigated three screening methods using the Mini-Mental State Examination (MMSE) as the cognitive test, and the Korean dementia screening questionnaire (KDSQ) as the informant report. Participants were recruited from the Korea Clinical Research Center for Dementia of South Korea, and included 2861 patients with Alzheimer's disease (dementia), 3519 patients with mild cognitive impairment and 1375 controls with no cognitive dysfunction. Three screening methods were tested: (i) MMSE alone (MMSE(cut-off) ); (ii) a conventional combination of MMSE and KDSQ (MMSE+KDSQ(cut-off) ); and (iii) a decision tree with MMSE and KDSQ (MMSE+KDSQ(decision tree) ). For discriminating any cognitive dysfunction from controls, MMSE+KDSQ(cut-off) had the highest area under the receiver operating characteristic curve (0.784). For discriminating dementia from controls, MMSE+KDSQ(cut-off) had the highest area under the receiver operating characteristic curve (0.899). For discriminating mild cognitive impairment from controls, MMSE(cut-off) had the highest area under the receiver operating characteristic curve (0.683). MMSE+KDSQ(decision tree) showed the highest sensitivity for all discriminations. For overall classification accuracy, MMSE+KDSQ(decision tree) had the highest value (70.0%). These three methods had different advantageous properties for screening and staging cognitive dysfunction. As there might be different availability across clinical settings, these three methods can be selected and used according to situational needs. © 2015 Japan Geriatrics Society.

The risk factors of laryngeal pathology in Korean adults using a decision tree model.

PubMed

Byeon, Haewon

2015-01-01

The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models

NASA Astrophysics Data System (ADS)

Saha, Debasish; Kemanian, Armen R.; Rau, Benjamin M.; Adler, Paul R.; Montes, Felipe

2017-04-01

Annual cumulative soil nitrous oxide (N2O) emissions calculated from discrete chamber-based flux measurements have unknown uncertainty. We used outputs from simulations obtained with an agroecosystem model to design sampling strategies that yield accurate cumulative N2O flux estimates with a known uncertainty level. Daily soil N2O fluxes were simulated for Ames, IA (corn-soybean rotation), College Station, TX (corn-vetch rotation), Fort Collins, CO (irrigated corn), and Pullman, WA (winter wheat), representing diverse agro-ecoregions of the United States. Fertilization source, rate, and timing were site-specific. These simulated fluxes surrogated daily measurements in the analysis. We ;sampled; the fluxes using a fixed interval (1-32 days) or a rule-based (decision tree-based) sampling method. Two types of decision trees were built: a high-input tree (HI) that included soil inorganic nitrogen (SIN) as a predictor variable, and a low-input tree (LI) that excluded SIN. Other predictor variables were identified with Random Forest. The decision trees were inverted to be used as rules for sampling a representative number of members from each terminal node. The uncertainty of the annual N2O flux estimation increased along with the fixed interval length. A 4- and 8-day fixed sampling interval was required at College Station and Ames, respectively, to yield ±20% accuracy in the flux estimate; a 12-day interval rendered the same accuracy at Fort Collins and Pullman. Both the HI and the LI rule-based methods provided the same accuracy as that of fixed interval method with up to a 60% reduction in sampling events, particularly at locations with greater temporal flux variability. For instance, at Ames, the HI rule-based and the fixed interval methods required 16 and 91 sampling events, respectively, to achieve the same absolute bias of 0.2 kg N ha-1 yr-1 in estimating cumulative N2O flux. These results suggest that using simulation models along with decision trees can reduce the cost and improve the accuracy of the estimations of cumulative N2O fluxes using the discrete chamber-based method.

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

Effects of preheat and mix on the fuel adiabat of an imploding capsule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, B.; Kwan, T. J. T.; Wang, Y. M.

We demonstrate the effect of preheat, hydrodynamic mix and vorticity on the adiabat of the deuterium-tritium (DT) fuel in fusion capsule experiments. We show that the adiabat of the DT fuel increases resulting from hydrodynamic mixing due to the phenomenon of entropy of mixture. An upper limit of mix, M clean=M DT ≥ 0:98 is found necessary to keep the DT fuel on a low adiabat. We demonstrate in this study that the use of a high adiabat for the DT fuel in theoretical analysis and with the aid of 1D code simulations could explain some aspects of 3D effectsmore » and mix in capsule implosion. Furthermore, we can infer from our physics model and the observed neutron images the adiabat of the DT fuel in the capsule and the amount of mix produced on the hot spot.« less

One-Year-Olds Think Creatively, Just Like Their Parents.

PubMed

Hoicka, Elena; Mowat, Rachael; Kirkwood, Joanne; Kerr, Tiffany; Carberry, Megan; Bijvoet-van den Berg, Simone

2016-07-01

Creativity is an essential human ability, allowing adaptation and survival. Twenty-nine 1-year-olds and their parents were tested on divergent thinking (DT), a measure of creative potential counting how many ideas one can generate. Toddlers' and parents' DT was moderately to highly correlated. Toddlers showed a wide range of DT scores, which were reliable on retesting. This is the first study to show children think divergently as early as 1 year. This research also suggests 1-year-olds' DT is related to parents', opening up future research into whether this relationship is due to genetics and/or social learning at its emergence. Understanding DT at its emergence could allow for interventions while neurological development is most plastic, which could improve DT across the life span. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Effects of preheat and mix on the fuel adiabat of an imploding capsule

DOE PAGES

Cheng, B.; Kwan, T. J. T.; Wang, Y. M.; ...

2016-12-01

We demonstrate the effect of preheat, hydrodynamic mix and vorticity on the adiabat of the deuterium-tritium (DT) fuel in fusion capsule experiments. We show that the adiabat of the DT fuel increases resulting from hydrodynamic mixing due to the phenomenon of entropy of mixture. An upper limit of mix, M clean=M DT ≥ 0:98 is found necessary to keep the DT fuel on a low adiabat. We demonstrate in this study that the use of a high adiabat for the DT fuel in theoretical analysis and with the aid of 1D code simulations could explain some aspects of 3D effectsmore » and mix in capsule implosion. Furthermore, we can infer from our physics model and the observed neutron images the adiabat of the DT fuel in the capsule and the amount of mix produced on the hot spot.« less