A New Measure for Assessing the Contributions of Higher Level Processes to Language Comprehension Performance in Preschoolers

ERIC Educational Resources Information Center

Hannon, Brenda; Frias, Sarah

2012-01-01

The present study reports the development of a theoretically motivated measure that provides estimates of a preschooler's ability to recall auditory text, to make text-based inferences, to access knowledge from long-term memory, and to integrate this accessed knowledge with new information from auditory text. This new preschooler component…

Managing the data deluge: data-driven GO category assignment improves while complexity of functional annotation increases.

PubMed

Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

2013-01-01

The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based--or dictionary-based--approaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists. DATABASE URL: http://eagl.unige.ch/GOCat/

Knowledge Based Software Assistant Conference Proceedings (4th) Held in Syracuse, New York on 12-14 September 1989

DTIC Science & Technology

1990-05-01

Sanders Associates. Inc. A demonstration of knowledge-based support for the evolut ;cnry development of software system requirements uskig mitV/9 text...Conference Commiffee W Douga W~t Spin-Off Technologies 4 AN OVERVIEW OF RADC’S KNOWLEDGE BASED SOFTWARE ASSISTANT PROGRAM Donald M. Elefante Rome Air...Knowledge-Based Software Assistant is a formally based, computer-mediated paradigm for the specification, development, evolution , and Ir ig term

Integration of Text- and Data-Mining Technologies for Use in Banking Applications

NASA Astrophysics Data System (ADS)

Maslankowski, Jacek

Unstructured data, most of it in the form of text files, typically accounts for 85% of an organization's knowledge stores, but it's not always easy to find, access, analyze or use (Robb 2004). That is why it is important to use solutions based on text and data mining. This solution is known as duo mining. This leads to improve management based on knowledge owned in organization. The results are interesting. Data mining provides to lead with structuralized data, usually powered from data warehouses. Text mining, sometimes called web mining, looks for patterns in unstructured data — memos, document and www. Integrating text-based information with structured data enriches predictive modeling capabilities and provides new stores of insightful and valuable information for driving business and research initiatives forward.

Extracting semantically enriched events from biomedical literature

PubMed Central

2012-01-01

Background Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Results Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. Conclusions We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare. PMID:22621266

Extracting semantically enriched events from biomedical literature.

PubMed

Miwa, Makoto; Thompson, Paul; McNaught, John; Kell, Douglas B; Ananiadou, Sophia

2012-05-23

Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.

Semi-Automated Methods for Refining a Domain-Specific Terminology Base

DTIC Science & Technology

2011-02-01

only as a resource for written and oral translation, but also for Natural Language Processing ( NLP ) applications, text retrieval, document indexing...Natural Language Processing ( NLP ) applications, text retrieval, document indexing, and other knowledge management tasks. The objective of this...also for Natural Language Processing ( NLP ) applications, text retrieval (1), document indexing, and other knowledge management tasks. The National

Reading and reading instruction for children from low-income and non-English-speaking households.

PubMed

Lesaux, Nonie K

2012-01-01

Although most young children seem to master reading skills in the early grades of elementary school, many struggle with texts as they move through middle school and high school. Why do children who seem to be proficient readers in third grade have trouble comprehending texts in later grades? To answer this question, Nonie Lesaux describes what is known about reading development and instruction, homing in on research conducted with children from low-income and non-English-speaking homes. Using key insights from this research base, she offers two explanations. The first is that reading is a dynamic and multifaceted process that requires continued development if students are to keep pace with the increasing demands of school texts and tasks. The second lies in the role of reading assessment and instruction in U.S. schools. Lesaux draws a distinction between the "skills-based competencies" that readers need to sound out and recognize words and the "knowledge-based competencies" that include the conceptual and vocabulary knowledge necessary to comprehend a text's meaning. Although U.S. schools have made considerable progress in teaching skills-based reading competencies that are the focus of the early grades, most have made much less progress in teaching the knowledge-based competencies students need to support reading comprehension in middle and high school. These knowledge-based competencies are key sources of lasting individual differences in reading outcomes, particularly among children growing up in low-income and non-English-speaking households. Augmenting literacy rates, Lesaux explains, will require considerable shifts in the way reading is assessed and taught in elementary and secondary schools. First, schools must conduct comprehensive reading assessments that discern learners' (potential) sources of reading difficulties--in both skills-based and knowledge-based competencies. Second, educators must implement instructional approaches that offer promise for teaching the conceptual and knowledge-based reading competencies that are critical for academic success, particularly for academically vulnerable populations.

Improving Collaborative Learning in the Classroom: Text Mining Based Grouping and Representing

ERIC Educational Resources Information Center

Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich

2016-01-01

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…

Knowledge-Driven Event Extraction in Russian: Corpus-Based Linguistic Resources

PubMed Central

Solovyev, Valery; Ivanov, Vladimir

2016-01-01

Automatic event extraction form text is an important step in knowledge acquisition and knowledge base population. Manual work in development of extraction system is indispensable either in corpus annotation or in vocabularies and pattern creation for a knowledge-based system. Recent works have been focused on adaptation of existing system (for extraction from English texts) to new domains. Event extraction in other languages was not studied due to the lack of resources and algorithms necessary for natural language processing. In this paper we define a set of linguistic resources that are necessary in development of a knowledge-based event extraction system in Russian: a vocabulary of subordination models, a vocabulary of event triggers, and a vocabulary of Frame Elements that are basic building blocks for semantic patterns. We propose a set of methods for creation of such vocabularies in Russian and other languages using Google Books NGram Corpus. The methods are evaluated in development of event extraction system for Russian. PMID:26955386

Reading for meaning: The foundational knowledge every teacher of science should have

NASA Astrophysics Data System (ADS)

Patterson, Alexis; Roman, Diego; Friend, Michelle; Osborne, Jonathan; Donovan, Brian

2018-02-01

Reading is fundamental to science and not an adjunct to its practice. In other words, understanding the meaning of the various forms of written discourse employed in the creation, discussion, and communication of scientific knowledge is inherent to how science works. The language used in science, however, sets up a barrier, that in order to be overcome requires all students to have a clear understanding of the features of the multimodal informational texts employed in science and the strategies they can use to decode the scientific concepts communicated in informational texts. We argue that all teachers of science must develop a functional understanding of reading comprehension as part of their professional knowledge and skill. After describing our rationale for including knowledge about reading as a professional knowledge base every teacher of science should have, we outline the knowledge about language teachers must develop, the knowledge about the challenges that reading comprehension of science texts poses for students, and the knowledge about instructional strategies science teachers should know to support their students' reading comprehension of science texts. Implications regarding the essential role that knowledge about reading should play in the preparation of science teachers are also discussed here.

A knowledge engineering approach to recognizing and extracting sequences of nucleic acids from scientific literature.

PubMed

García-Remesal, Miguel; Maojo, Victor; Crespo, José

2010-01-01

In this paper we present a knowledge engineering approach to automatically recognize and extract genetic sequences from scientific articles. To carry out this task, we use a preliminary recognizer based on a finite state machine to extract all candidate DNA/RNA sequences. The latter are then fed into a knowledge-based system that automatically discards false positives and refines noisy and incorrectly merged sequences. We created the knowledge base by manually analyzing different manuscripts containing genetic sequences. Our approach was evaluated using a test set of 211 full-text articles in PDF format containing 3134 genetic sequences. For such set, we achieved 87.76% precision and 97.70% recall respectively. This method can facilitate different research tasks. These include text mining, information extraction, and information retrieval research dealing with large collections of documents containing genetic sequences.

n-Gram-Based Indexing for Korean Text Retrieval.

ERIC Educational Resources Information Center

Lee, Joon Ho; Cho, Hyun Yang; Park, Hyouk Ro

1999-01-01

Discusses indexing methods in Korean text retrieval and proposes a new indexing method based on n-grams which can handle compound nouns effectively without dictionaries and complex linguistic knowledge. Experimental results show that n-gram-based indexing is considerably faster than morpheme-based indexing, and also provides better retrieval…

Knowledge, its Application, and Attitudes Associated with the Reading of Diverse Genres of Science Texts

NASA Astrophysics Data System (ADS)

Gonçalves Nigro, Rogerio; Frateschi Trivelato, Silvia

2012-11-01

The purpose of this article is to assess the knowledge, application of knowledge, and attitudes associated with the reading of different genres of expository science texts. We assigned approximately half of a sample consisting of 220 students 14-15 years of age, chosen at random, to read an excerpt from a popular scientific text, and the other half to read an excerpt from a textbook addressing the same topic. Readers took knowledge and application tests immediately after the reading and again 15 days later. Students also took knowledge and reading proficiency pre-tests, and attitude tests related to the selected texts. Overall, girls scored higher than boys and readers of the popular scientific text scored higher than their colleagues who read the textbook excerpt. We noted interaction between 'reader gender' and 'genre of the text read' in terms of long-term learning based on the reading. Attitude regarding the text read appears as an important factor in explaining behavior of boys who read the popular scientific text. Surprisingly, knowledge and application test scores were not statistically different among girls with different degrees of reading proficiency who read the textbook excerpt. In addition, on the application tests, among the boys who read the popular scientific text, good readers scored lower than their colleagues who read the textbook excerpt. In our opinion, this study can serve to show that 'reading in science education' is not a trivial matter and we feel that the subject merits more in-depth investigation.

An architecture for rule based system explanation

NASA Technical Reports Server (NTRS)

Fennel, T. R.; Johannes, James D.

1990-01-01

A system architecture is presented which incorporate both graphics and text into explanations provided by rule based expert systems. This architecture facilitates explanation of the knowledge base content, the control strategies employed by the system, and the conclusions made by the system. The suggested approach combines hypermedia and inference engine capabilities. Advantages include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. User models are suggested to control the type, amount, and order of information presented.

Message Design Guidelines For Screen-Based Programs.

ERIC Educational Resources Information Center

Rimar, G. I.

1996-01-01

Effective message design for screen-based computer or video instructional programs requires knowledge from many disciplines. Evaluates current conventions and suggests a new set of guidelines for screen-based designers. Discusses screen layout, highlighting and cueing, text font and style, text positioning, color, and graphical user interfaces for…

Content-based image retrieval with ontological ranking

NASA Astrophysics Data System (ADS)

Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.

2010-02-01

Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping is different from pure visual similarity clustering. More specifically, the inferred concepts of each image in the group are examined in the context of a huge concept ontology to determine their true relations with what people have in mind when doing image search.

Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life

PubMed Central

Thessen, Anne E.; Parr, Cynthia Sims

2014-01-01

Numerous digitization and ontological initiatives have focused on translating biological knowledge from narrative text to machine-readable formats. In this paper, we describe two workflows for knowledge extraction and semantic annotation of text data objects featured in an online biodiversity aggregator, the Encyclopedia of Life. One workflow tags text with DBpedia URIs based on keywords. Another workflow finds taxon names in text using GNRD for the purpose of building a species association network. Both workflows work well: the annotation workflow has an F1 Score of 0.941 and the association algorithm has an F1 Score of 0.885. Existing text annotators such as Terminizer and DBpedia Spotlight performed well, but require some optimization to be useful in the ecology and evolution domain. Important future work includes scaling up and improving accuracy through the use of distributional semantics. PMID:24594988

The influence of text cohesion and picture detail on young readers' knowledge of science topics.

PubMed

Désiron, Juliette C; de Vries, Erica; Bartel, Anna N; Varahamurti, Nalini

2017-10-16

The effects of text cohesion and added pictures on acquired knowledge have been heavily studied each in isolation. Furthermore, studies on the effects of specific characteristics of pictures, whether facilitating or hindering, are scarce. Schnotz's ITCP Model (2014) allows to formulate hypotheses regarding the combined effect of text cohesion and presence and level of detail of a picture. This study investigates these hypotheses in the case of children reading scientific texts. One hundred and one-second-, third-, and fourth-grade pupils with a mean age of 9 years, in the western United States. Data were collected over three sessions to encompass an understanding of each pupil's knowledge based on prior sessions. Results showed a significant increase in pupils' knowledge between pre-test and immediate post-test, but as hypothesized, no significant difference between levels of cohesion. No significant difference between types of pictures was detected. After 1 week, knowledge built with a high cohesive text significantly dropped with low-detail picture, whereas, with high detail, or no picture, there was no significant difference. Results suggested that when participants were given a low-detail picture with a low cohesive text, the integration process of the material was more restricted than with a high cohesive text. © 2017 The British Psychological Society.

Visualizing complex processes using a cognitive-mapping tool to support the learning of clinical reasoning.

PubMed

Wu, Bian; Wang, Minhong; Grotzer, Tina A; Liu, Jun; Johnson, Janice M

2016-08-22

Practical experience with clinical cases has played an important role in supporting the learning of clinical reasoning. However, learning through practical experience involves complex processes difficult to be captured by students. This study aimed to examine the effects of a computer-based cognitive-mapping approach that helps students to externalize the reasoning process and the knowledge underlying the reasoning process when they work with clinical cases. A comparison between the cognitive-mapping approach and the verbal-text approach was made by analyzing their effects on learning outcomes. Fifty-two third-year or higher students from two medical schools participated in the study. Students in the experimental group used the computer-base cognitive-mapping approach, while the control group used the verbal-text approach, to make sense of their thinking and actions when they worked with four simulated cases over 4 weeks. For each case, students in both groups reported their reasoning process (involving data capture, hypotheses formulation, and reasoning with justifications) and the underlying knowledge (involving identified concepts and the relationships between the concepts) using the given approach. The learning products (cognitive maps or verbal text) revealed that students in the cognitive-mapping group outperformed those in the verbal-text group in the reasoning process, but not in making sense of the knowledge underlying the reasoning process. No significant differences were found in a knowledge posttest between the two groups. The computer-based cognitive-mapping approach has shown a promising advantage over the verbal-text approach in improving students' reasoning performance. Further studies are needed to examine the effects of the cognitive-mapping approach in improving the construction of subject-matter knowledge on the basis of practical experience.

Comprehension and retrieval of failure cases in airborne observatories

NASA Technical Reports Server (NTRS)

Alvarado, Sergio J.; Mock, Kenrick J.

1995-01-01

This paper describes research dealing with the computational problem of analyzing and repairing failures of electronic and mechanical systems of telescopes in NASA's airborne observatories, such as KAO (Kuiper Airborne Observatory) and SOFIA (Stratospheric Observatory for Infrared Astronomy). The research has resulted in the development of an experimental system that acquires knowledge of failure analysis from input text, and answers questions regarding failure detection and correction. The system's design builds upon previous work on text comprehension and question answering, including: knowledge representation for conceptual analysis of failure descriptions, strategies for mapping natural language into conceptual representations, case-based reasoning strategies for memory organization and indexing, and strategies for memory search and retrieval. These techniques have been combined into a model that accounts for: (a) how to build a knowledge base of system failures and repair procedures from descriptions that appear in telescope-operators' logbooks and FMEA (failure modes and effects analysis) manuals; and (b) how to use that knowledge base to search and retrieve answers to questions about causes and effects of failures, as well as diagnosis and repair procedures. This model has been implemented in FANSYS (Failure ANalysis SYStem), a prototype text comprehension and question answering program for failure analysis.

Comprehension and retrieval of failure cases in airborne observatories

NASA Astrophysics Data System (ADS)

Alvarado, Sergio J.; Mock, Kenrick J.

1995-05-01

This paper describes research dealing with the computational problem of analyzing and repairing failures of electronic and mechanical systems of telescopes in NASA's airborne observatories, such as KAO (Kuiper Airborne Observatory) and SOFIA (Stratospheric Observatory for Infrared Astronomy). The research has resulted in the development of an experimental system that acquires knowledge of failure analysis from input text, and answers questions regarding failure detection and correction. The system's design builds upon previous work on text comprehension and question answering, including: knowledge representation for conceptual analysis of failure descriptions, strategies for mapping natural language into conceptual representations, case-based reasoning strategies for memory organization and indexing, and strategies for memory search and retrieval. These techniques have been combined into a model that accounts for: (a) how to build a knowledge base of system failures and repair procedures from descriptions that appear in telescope-operators' logbooks and FMEA (failure modes and effects analysis) manuals; and (b) how to use that knowledge base to search and retrieve answers to questions about causes and effects of failures, as well as diagnosis and repair procedures. This model has been implemented in FANSYS (Failure ANalysis SYStem), a prototype text comprehension and question answering program for failure analysis.

An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

PubMed

Goldstein, Ayelet; Shahar, Yuval

2016-06-01

Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different complex contexts, and a suitable medical knowledge base. The system then automatically generates a textual summary of the data. We aim to prove the feasibility of implementing such a system, and to demonstrate its potential benefits for clinicians and for enhancement of quality of care. We have designed a new, domain-independent, knowledge-based system, the CliniText system, for automated summarization in free text of longitudinal medical records of any duration, in any context. The system is composed of six components: (1) A temporal abstraction module generates all possible abstractions from the patient's raw data using a temporal-abstraction knowledge base; (2) The abductive reasoning module infers abstractions or events from the data, which were not explicitly included in the database; (3) The pruning module filters out raw or abstract data based on predefined heuristics; (4) The document structuring module organizes the remaining raw or abstract data, according to the desired format; (5) The microplanning module, groups the raw or abstract data and creates referring expressions; (6) The surface realization module, generates the text, and applies the grammar rules of the chosen language. We have performed an initial technical evaluation of the system in the cardiac intensive-care and diabetes domains. We also summarize the results of a more detailed evaluation study that we have performed in the intensive-care domain that assessed the completeness, correctness, and overall quality of the system's generated text, and its potential benefits to clinical decision making. We assessed these measures for 31 letters originally composed by clinicians, and for the same letters when generated by the CliniText system. We have successfully implemented all of the components of the CliniText system in software. We have also been able to create a comprehensive temporal-abstraction knowledge base to support its functionality, mostly in the intensive-care domain. The initial technical evaluation of the system in the cardiac intensive-care and diabetes domains has shown great promise, proving the feasibility of constructing and operating such systems. The detailed results of the evaluation in the intensive-care domain are out of scope of the current paper, and we refer the reader to a more detailed source. In all of the letters composed by clinicians, there were at least two important items per letter missed that were included by the CliniText system. The clinicians' letters got a significantly better grade in three out of four measured quality parameters, as judged by an expert; however, the variance in the quality was much higher in the clinicians' letters. In addition, three clinicians answered questions based on the discharge letter 40% faster, and answered four out of the five questions equally well or significantly better, when using the CliniText-generated letters, than when using the clinician-composed letters. Constructing a working system for automated summarization in free text of large numbers of varying periods of multivariate longitudinal clinical data is feasible. So is the construction of a large knowledge base, designed to support such a system, in a complex clinical domain, such as the intensive-care domain. The integration of the quality and functionality results suggests that the optimal discharge letter should exploit both human and machine, possibly by creating a machine-generated draft that will be polished by a human clinician. Copyright © 2016 Elsevier Inc. All rights reserved.

The Use of Computer-Based Videogames in Knowledge Acquisition and Retention.

ERIC Educational Resources Information Center

Ricci, Katrina E.

1994-01-01

Research conducted at the Naval Training Systems Center in Orlando, Florida, investigated the acquisition and retention of basic knowledge with subject matter presented in the forms of text, test, and game. Results are discussed in terms of the effectiveness of computer-based games for military training. (Author/AEF)

Adaptation of Educational Text to an Open Interactive Learning System: A Case Study for ReTuDiS

ERIC Educational Resources Information Center

Samarakou, M.; Fylladitakis, E. D.; Tsaganou, G.; Gelegenis, J.; Karolidis, D.; Prentakis, P.

2013-01-01

Theoretical education is mainly based on university text-books, which usually include texts not structured according to any theory of text comprehension. Structuring a text is a demanding process. Text should be organized and structured in order to include descriptions on micro and macro-level representation of the knowledge domain. Since this is…

Text Signals Influence Second Language Expository Text Comprehension: Knowledge Structure Analysis

ERIC Educational Resources Information Center

Kim, Kyung; Clariana, Roy B.

2017-01-01

This quasi-experimental investigation describes the influence of text signals on second language expository science text comprehension. In two course sections, mixed proficiency Korean English language learners (n = 88) read one of two print-based English expository text passage versions. Participants in one section (n = 44) were given a version…

Text-based discovery in biomedicine: the architecture of the DAD-system.

PubMed

Weeber, M; Klein, H; Aronson, A R; Mork, J G; de Jong-van den Berg, L T; Vos, R

2000-01-01

Current scientific research takes place in highly specialized contexts with poor communication between disciplines as a likely consequence. Knowledge from one discipline may be useful for the other without researchers knowing it. As scientific publications are a condensation of this knowledge, literature-based discovery tools may help the individual scientist to explore new useful domains. We report on the development of the DAD-system, a concept-based Natural Language Processing system for PubMed citations that provides the biomedical researcher such a tool. We describe the general architecture and illustrate its operation by a simulation of a well-known text-based discovery: The favorable effects of fish oil on patients suffering from Raynaud's disease [1].

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.

Multi-frame knowledge based text enhancement for mobile phone captured videos

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-02-01

In this study, we explore automated text recognition and enhancement using mobile phone captured videos of store receipts. We propose a method which includes Optical Character Resolution (OCR) enhanced by our proposed Row Based Multiple Frame Integration (RB-MFI), and Knowledge Based Correction (KBC) algorithms. In this method, first, the trained OCR engine is used for recognition; then, the RB-MFI algorithm is applied to the output of the OCR. The RB-MFI algorithm determines and combines the most accurate rows of the text outputs extracted by using OCR from multiple frames of the video. After RB-MFI, KBC algorithm is applied to these rows to correct erroneous characters. Results of the experiments show that the proposed video-based approach which includes the RB-MFI and the KBC algorithm increases the word character recognition rate to 95%, and the character recognition rate to 98%.

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

PubMed Central

Gan, Liang; Cheng, Mian; Wu, Quanyuan

2018-01-01

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking. PMID:29849994

Prior Knowledge and Online Inquiry-Based Science Reading: Evidence from Eye Tracking

ERIC Educational Resources Information Center

Ho, Hsin Ning Jessie; Tsai, Meng-Jung; Wang, Ching-Yeh; Tsai, Chin-Chung

2014-01-01

This study employed eye-tracking technology to examine how students with different levels of prior knowledge process text and data diagrams when reading a web-based scientific report. Students' visual behaviors were tracked and recorded when they read a report demonstrating the relationship between the greenhouse effect and global climate…

Two Different Approaches to Automated Mark Up of Emotions in Text

NASA Astrophysics Data System (ADS)

Francisco, Virginia; Hervás, Raqucl; Gervás, Pablo

This paper presents two different approaches to automated marking up of texts with emotional labels. For the first approach a corpus of example texts previously annotated by human evaluators is mined for an initial assignment of emotional features to words. This results in a List of Emotional Words (LEW) which becomes a useful resource for later automated mark up. The mark up algorithm in this first approach mirrors closely the steps taken during feature extraction, employing for the actual assignment of emotional features a combination of the LEW resource and WordNet for knowledge-based expansion of words not occurring in LEW. The algorithm for automated mark up is tested against new text samples to test its coverage. The second approach mark up texts during their generation. We have a knowledge base which contains the necessary information for marking up the text. This information is related to actions and characters. The algorithm in this case employ the information of the knowledge database and decides the correct emotion for every sentence. The algorithm for automated mark up is tested against four different texts. The results of the two approaches are compared and discussed with respect to three main issues: relative adequacy of each one of the representations used, correctness and coverage of the proposed algorithms, and additional techniques and solutions that may be employed to improve the results.

Randomized Trial of a Web-Based Intervention to Address Barriers to Clinical Trials.

PubMed

Meropol, Neal J; Wong, Yu-Ning; Albrecht, Terrance; Manne, Sharon; Miller, Suzanne M; Flamm, Anne Lederman; Benson, Al Bowen; Buzaglo, Joanne; Collins, Michael; Egleston, Brian; Fleisher, Linda; Katz, Michael; Kinzy, Tyler G; Liu, Tasnuva M; Margevicius, Seunghee; Miller, Dawn M; Poole, David; Roach, Nancy; Ross, Eric; Schluchter, Mark D

2016-02-10

Lack of knowledge and negative attitudes have been identified as barriers to participation in clinical trials by patients with cancer. We developed Preparatory Education About Clinical Trials (PRE-ACT), a theory-guided, Web-based, interactive computer program, to deliver tailored video educational content to patients in an effort to overcome barriers to considering clinical trials as a treatment option. A prospective, randomized clinical trial compared PRE-ACT with a control condition that provided general clinical trials information produced by the National Cancer Institute (NCI) in text format. One thousand two hundred fifty-five patients with cancer were randomly allocated before their initial visit with an oncologist to PRE-ACT (n = 623) or control (n = 632). PRE-ACT had three main components: assessment of clinical trials knowledge and attitudinal barriers, values assessment with clarification back to patients, and provision of a video library tailored to address each patient's barriers. Outcomes included knowledge and attitudes and preparation for decision making about clinical trials. Both PRE-ACT and control interventions improved knowledge and attitudes (all P < .001) compared with baseline. Patients randomly allocated to PRE-ACT showed a significantly greater increase in knowledge (P < .001) and a significantly greater decrease in attitudinal barriers (P < .001) than did their control (text-only) counterparts. Participants in both arms significantly increased their preparedness to consider clinical trials (P < .001), and there was a trend favoring the PRE-ACT group (P < .09). PRE-ACT was also associated with greater patient satisfaction than was NCI text alone. These data show that patient education before the first oncologist visit improves knowledge, attitudes, and preparation for decision making about clinical trials. Both text and tailored video were effective. The PRE-ACT interactive video program was more effective than NCI text in improving knowledge and reducing attitudinal barriers. © 2015 by American Society of Clinical Oncology.

Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

PubMed

Zheng, Wu; Blake, Catherine

2015-10-01

Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts. Copyright © 2015 Elsevier Inc. All rights reserved.

Effectiveness of false correction strategy on science reading comprehension

NASA Astrophysics Data System (ADS)

Ghent, Cynthia Anne

False-correction reading strategy theoretically prompted college students to activate their prior knowledge when provided false statements linked to a portion of their biology textbook. This strategy is based in elaborative interrogation theory, which suggests that prompting readers to answer interrogatives about text students are reading increases their comprehension of that text. These interrogatives always asked "why" statements pulled from a text, one sentence in length, were "true." True statements in this study based on a text were converted by the experimenter into false statements, one sentence in length. Students were requested to rewrite each statement (n=12) on average every 200 words in a text as they were reading, converting each false statement into a true statement. These students outperformed other students requested to reread the same biology text twice (an established placebo-control strategy). These students, in turn, outperformed still other students reading an unrelated control text taken from the same textbook used only to establish a prior knowledge baseline for all students included in this study. Students participating in this study were enrolled students in an undergraduate introductory general biology course designed for non-majors. A three-group, posttest-only, randomized experimental control-group design was used to prevent pretest activation of students' prior knowledge thus increasing chances of producing evidence of false-correction effectiveness and to begin augmenting potential generalizability to science classrooms. Students' (n=357) general biology knowledge, verbal ability, and attempts to use the false correction strategy were collected and analyzed. Eight of the participants were interviewed by the researcher in a first attempt in this domain to collect data on participants' points of view about the strategy. The results of this study are not yet recommended for use in authentic school settings as further research is indicated.

Patterns of Debate in Tertiary Level Asynchronous Text-Based Conferencing

ERIC Educational Resources Information Center

Coffin, Caroline; Painter, Clare; Hewings, Ann

2005-01-01

Argumentation can be defined at different levels and serve different purposes, but its role in knowledge understanding and construction has given it a central place in education, particularly at tertiary level. The advent of computer-supported text-based conferences has created new sites where such educational dialogues can take place, but the…

Case-based e-learning to improve the attitude of medical students towards occupational health, a randomised controlled trial.

PubMed

Smits, P B A; de Graaf, L; Radon, K; de Boer, A G; Bos, N R; van Dijk, F J H; Verbeek, J H A M

2012-04-01

Undergraduate medical teaching in occupational health (OH) is a challenge in universities around the world. Case-based e-learning with an attractive clinical context could improve the attitude of medical students towards OH. The study question is whether case-based e-learning for medical students is more effective in improving knowledge, satisfaction and a positive attitude towards OH than non-case-based textbook learning. Participants, 141 second year medical students, were randomised to either case-based e-learning or text-based learning. Outcome measures were knowledge, satisfaction and attitude towards OH, measured at baseline, directly after the intervention, after 1 week and at 3-month follow-up. Of the 141 participants, 130 (92%) completed the questionnaires at short-term follow-up and 41 (29%) at 3-month follow-up. At short-term follow-up, intervention and control groups did not show a significant difference in knowledge nor satisfaction but attitude towards OH was significantly more negative in the intervention group (F=4.041, p=0.047). At 3-month follow-up, there were no significant differences between intervention and control groups for knowledge, satisfaction and attitude. We found a significant decrease in favourable attitude during the internship in the experimental group compared with the control group. There were no significant differences in knowledge or satisfaction between case-based e-learning and text-based learning. The attitude towards OH should be further investigated as an outcome of educational programmes.

Consider the source: Children link the accuracy of text-based sources to the accuracy of the author.

PubMed

Vanderbilt, Kimberly E; Ochoa, Karlena D; Heilbrun, Jayd

2018-05-06

The present research investigated whether young children link the accuracy of text-based information to the accuracy of its author. Across three experiments, three- and four-year-olds (N = 231) received information about object labels from accurate and inaccurate sources who provided information both in text and verbally. Of primary interest was whether young children would selectively rely on information provided by more accurate sources, regardless of the form in which the information was communicated. Experiment 1 tested children's trust in text-based information (e.g., books) written by an author with a history of either accurate or inaccurate verbal testimony and found that children showed greater trust in books written by accurate authors. Experiment 2 replicated the findings of Experiment 1 and extended them by showing that children's selective trust in more accurate text-based sources was not dependent on experience trusting or distrusting the author's verbal testimony. Experiment 3 investigated this understanding in reverse by testing children's trust in verbal testimony communicated by an individual who had authored either accurate or inaccurate text-based information. Experiment 3 revealed that children showed greater trust in individuals who had authored accurate rather than inaccurate books. Experiment 3 also demonstrated that children used the accuracy of text-based sources to make inferences about the mental states of the authors. Taken together, these results suggest children do indeed link the reliability of text-based sources to the reliability of the author. Statement of Contribution Existing knowledge Children use sources' prior accuracy to predict future accuracy in face-to-face verbal interactions. Children who are just learning to read show increased trust in text bases (vs. verbal) information. It is unknown whether children consider authors' prior accuracy when judging the accuracy of text-based information. New knowledge added by this article Preschool children track sources' accuracy across communication mediums - from verbal to text-based modalities and vice versa. Children link the reliability of text-based sources to the reliability of the author. © 2018 The British Psychological Society.

Valx: A system for extracting and structuring numeric lab test comparison statements from text

PubMed Central

Hao, Tianyong; Liu, Hongfang; Weng, Chunhua

2017-01-01

Objectives To develop an automated method for extracting and structuring numeric lab test comparison statements from text and evaluate the method using clinical trial eligibility criteria text. Methods Leveraging semantic knowledge from the Unified Medical Language System (UMLS) and domain knowledge acquired from the Internet, Valx takes 7 steps to extract and normalize numeric lab test expressions: 1) text preprocessing, 2) numeric, unit, and comparison operator extraction, 3) variable identification using hybrid knowledge, 4) variable - numeric association, 5) context-based association filtering, 6) measurement unit normalization, and 7) heuristic rule-based comparison statements verification. Our reference standard was the consensus-based annotation among three raters for all comparison statements for two variables, i.e., HbA1c and glucose, identified from all of Type 1 and Type 2 diabetes trials in ClinicalTrials.gov. Results The precision, recall, and F-measure for structuring HbA1c comparison statements were 99.6%, 98.1%, 98.8% for Type 1 diabetes trials, and 98.8%, 96.9%, 97.8% for Type 2 Diabetes trials, respectively. The precision, recall, and F-measure for structuring glucose comparison statements were 97.3%, 94.8%, 96.1% for Type 1 diabetes trials, and 92.3%, 92.3%, 92.3% for Type 2 diabetes trials, respectively. Conclusions Valx is effective at extracting and structuring free-text lab test comparison statements in clinical trial summaries. Future studies are warranted to test its generalizability beyond eligibility criteria text. The open-source Valx enables its further evaluation and continued improvement among the collaborative scientific community. PMID:26940748

Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text.

PubMed

Hao, Tianyong; Liu, Hongfang; Weng, Chunhua

2016-05-17

To develop an automated method for extracting and structuring numeric lab test comparison statements from text and evaluate the method using clinical trial eligibility criteria text. Leveraging semantic knowledge from the Unified Medical Language System (UMLS) and domain knowledge acquired from the Internet, Valx takes seven steps to extract and normalize numeric lab test expressions: 1) text preprocessing, 2) numeric, unit, and comparison operator extraction, 3) variable identification using hybrid knowledge, 4) variable - numeric association, 5) context-based association filtering, 6) measurement unit normalization, and 7) heuristic rule-based comparison statements verification. Our reference standard was the consensus-based annotation among three raters for all comparison statements for two variables, i.e., HbA1c and glucose, identified from all of Type 1 and Type 2 diabetes trials in ClinicalTrials.gov. The precision, recall, and F-measure for structuring HbA1c comparison statements were 99.6%, 98.1%, 98.8% for Type 1 diabetes trials, and 98.8%, 96.9%, 97.8% for Type 2 diabetes trials, respectively. The precision, recall, and F-measure for structuring glucose comparison statements were 97.3%, 94.8%, 96.1% for Type 1 diabetes trials, and 92.3%, 92.3%, 92.3% for Type 2 diabetes trials, respectively. Valx is effective at extracting and structuring free-text lab test comparison statements in clinical trial summaries. Future studies are warranted to test its generalizability beyond eligibility criteria text. The open-source Valx enables its further evaluation and continued improvement among the collaborative scientific community.

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

PubMed Central

Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon

2018-01-01

Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341

Cognitive Demand Differences in Causal Inferences: Characters' Plans Are More Difficult to Comprehend than Physical Causation

ERIC Educational Resources Information Center

Shears, Connie; Miller, Vanessa; Ball, Megan; Hawkins, Amanda; Griggs, Janna; Varner, Andria

2007-01-01

Readers may draw knowledge-based inferences to connect sentences in text differently depending on the knowledge domain being accessed. Most prior research has focused on the direction of the causal explanation (predictive vs. backward) without regard to the knowledge domain drawn on to support comprehension. We suggest that less cognitive effort…

Enhancing biomedical text summarization using semantic relation extraction.

PubMed

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.

Synchronous Web-Based Communication Using Text as a Means of Enhancing Discussion among School Students

ERIC Educational Resources Information Center

Fullick, Patrick L.

2006-01-01

Purpose: To explore the use of asynchronous and synchronous text-based communication using the internet as a means of promoting discussion among a knowledge-building community of school students carrying out a science investigation. Design/methodology/approach: Activity theory is used to bring together the various theories associated with the…

Studies of Human Memory and Language Processing.

ERIC Educational Resources Information Center

Collins, Allan M.

The purposes of this study were to determine the nature of human semantic memory and to obtain knowledge usable in the future development of computer systems that can converse with people. The work was based on a computer model which is designed to comprehend English text, relating the text to information stored in a semantic data base that is…

Web App- and Text Message-Based Patient Education in Mohs Micrographic Surgery-A Randomized Controlled Trial.

PubMed

Hawkins, Spencer D; Koch, Sarah B; Williford, Phillip M; Feldman, Steven R; Pearce, Daniel J

2018-07-01

Consent and wound care (WC) videos are used for education in Mohs micrographic surgery (MMS). Postoperative text messaging is poorly studied. Develop and evaluate perioperative resources for MMS patients-video modules (DermPatientEd.com) and postoperative text messaging (DermTexts.com). A study was conducted on 90 MMS patients. Patients were randomized 1:1:1:1 to videos with text messages, videos-only, text messages-only, or control. Primary outcomes included preoperative anxiety and knowledge of MMS and postoperative care. The secondary outcome included helpfulness/preference of interventions. Patients experienced a 19% reduction in anxiety as measured by a visual analog scale after the MMS video (p = .00062). There was no difference in knowledge after the WC video (p = .21498). Patients were more likely to report the WC video "very helpful" when compared with the pamphlet in understanding postoperative WC (p = .0016). Patients in text messaging groups were not more likely to report the service as "very helpful" when compared with the pamphlet (p = .3566), but preferred to receive WC instructions by text message for future visits (p = .0001). These resources proved helpful and effective in reducing preoperative anxiety. Patients prefer text message-based WC instructions over pamphlets after experiencing the service, but do not find them more helpful.

The Interplay of Firsthand and Text-Based Investigations in Science Education. Ciera Report.

ERIC Educational Resources Information Center

Palincsar, Annemarie Sullivan; Magnusson, Shirley J.

This paper presents the results of a study concerning the use of text in support of firsthand scientific inquiry instruction in the early elementary grades. A partial transcript of two teaching sessions in which an expert classroom teacher incorporated text into her inquiry instruction is investigated. The knowledge gained from these sessions…

Text mining patents for biomedical knowledge.

PubMed

Rodriguez-Esteban, Raul; Bundschus, Markus

2016-06-01

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning the Structure of Biomedical Relationships from Unstructured Text

PubMed Central

Percha, Bethany; Altman, Russ B.

2015-01-01

The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured text of over 23 million articles. The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions. Extracting these relationships from natural language sentences on such a large scale, however, requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas. Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice and sentence structure. We validate EBC's performance against manually-curated sets of (1) pharmacogenomic relationships from PharmGKB and (2) drug-target relationships from DrugBank, and use it to discover new drug-gene relationships for both knowledge bases. We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline, revealing unexpected structure that challenges current notions about how these relationships are expressed in text. For instance, we learn that newer experimental findings are described in consistently different ways than established knowledge, and that seemingly pure classes of relationships can exhibit interesting chimeric structure. The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining. PMID:26219079

Developing a geoscience knowledge framework for a national geological survey organisation

NASA Astrophysics Data System (ADS)

Howard, Andrew S.; Hatton, Bill; Reitsma, Femke; Lawrie, Ken I. G.

2009-04-01

Geological survey organisations (GSOs) are established by most nations to provide a geoscience knowledge base for effective decision-making on mitigating the impacts of natural hazards and global change, and on sustainable management of natural resources. The value of the knowledge base as a national asset is continually enhanced by the exchange of knowledge between GSOs as data and information providers and the stakeholder community as knowledge 'users and exploiters'. Geological maps and associated narrative texts typically form the core of national geoscience knowledge bases, but have some inherent limitations as methods of capturing and articulating knowledge. Much knowledge about the three-dimensional (3D) spatial interpretation and its derivation and uncertainty, and the wider contextual value of the knowledge, remains intangible in the minds of the mapping geologist in implicit and tacit form. To realise the value of these knowledge assets, the British Geological Survey (BGS) has established a workflow-based cyber-infrastructure to enhance its knowledge management and exchange capability. Future geoscience surveys in the BGS will contribute to a national, 3D digital knowledge base on UK geology, with the associated implicit and tacit information captured as metadata, qualitative assessments of uncertainty, and documented workflows and best practice. Knowledge-based decision-making at all levels of society requires both the accessibility and reliability of knowledge to be enhanced in the grid-based world. Establishment of collaborative cyber-infrastructures and ontologies for geoscience knowledge management and exchange will ensure that GSOs, as knowledge-based organisations, can make their contribution to this wider goal.

Randomized trial of two e-learning programs for oral health students on secondary prevention of eating disorders.

PubMed

DeBate, Rita D; Severson, Herbert H; Cragun, Deborah; Bleck, Jennifer; Gau, Jeff; Merrell, Laura; Cantwell, Carley; Christiansen, Steve; Koerber, Anne; Tomar, Scott L; Brown, Kelli McCormack; Tedesco, Lisa A; Hendricson, William; Taris, Mark

2014-01-01

The purpose of this study was to test whether an interactive, web-based training program is more effective than an existing, flat-text, e-learning program at improving oral health students' knowledge, motivation, and self-efficacy to address signs of disordered eating behaviors with patients. Eighteen oral health classes of dental and dental hygiene students were randomized to either the Intervention (interactive program; n=259) or Alternative (existing program; n=58) conditions. Hierarchical linear modeling assessed for posttest differences between groups while controlling for baseline measures. Improvement among Intervention participants was superior to those who completed the Alternative program for three of the six outcomes: benefits/barriers, self-efficacy, and skills-based knowledge (effect sizes ranging from 0.43 to 0.87). This study thus suggests that interactive training programs may be better than flat-text e-learning programs for improving the skills-based knowledge and self-efficacy necessary for behavior change.

Comprehension of University Texts: Effects of Domain-Knowledge and Summary

ERIC Educational Resources Information Center

Pascual, Gema; Goikoetxea, Edurne

2014-01-01

Our aim is to evaluate reading comprehension strategies based on empirical evidence and applicable to undergraduate students. Our hypotheses were that domain-knowledge or summary would have more influence on local, global, and inferential questions than rereading-question-answering instruction. Results of Experiment 1 were mixed in terms of…

A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text

ERIC Educational Resources Information Center

Nguyen, Bao-An; Yang, Don-Lin

2012-01-01

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with…

Epistemological Beliefs and Epistemic Strategies in Self-Regulated Learning

ERIC Educational Resources Information Center

Richter, Tobias; Schmid, Sebastian

2010-01-01

How do epistemological attitudes and beliefs influence learning from text? We conceptualize epistemological attitudes and beliefs as components of metacognitive knowledge. As such, they serve an important function in regulating the use of epistemic strategies such as knowledge-based validation of information and checking arguments for internal…

Literature-Based Instruction: Reshaping the Curriculum.

ERIC Educational Resources Information Center

Raphael, Taffy E., Ed.; Au, Kathryn H., Ed.

Making a case for the value of literature-based instruction, this book presents an overview of the extensive knowledge base supporting literature-based approaches to literacy instruction. It notes that literature-based instruction goes beyond simply changing the kinds of texts children read--also required in literature-based instruction are an…

The Interplay of Reader Goals, Working Memory, and Text Structure During Reading

PubMed Central

Bohn-Gettler, Catherine M.; Kendeou, Panayiota

2014-01-01

In the current study we examined the complex interactions of instructional context, text properties, and reader characteristics during comprehension. College students were tasked with the goal of reading for study versus entertainment (instructional context) while thinking-aloud about four different expository text structures (text properties). Working memory also was assessed (reader characteristics). Reading goals and working memory interacted to influence paraphrasing and non-coherence processes when thinking aloud. Reading goals, working memory, and text structure all interacted to influence text-based inferences. Text structure also influenced knowledge-based inferences. Post-reading recall was highest for those with the instructional goal of reading for study (compared to entertainment), as well as for problem-response and compare-contrast texts (compared to descriptive and chronological texts). Implications of the findings are discussed. PMID:25018581

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

PubMed Central

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization. PMID:21887336

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

PubMed

Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo

2018-01-05

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.

Designing Instructional Texts: Interaction between Text and Learner.

ERIC Educational Resources Information Center

Beukhof, Gijsbertus

A prescriptive theory for learning which delivers prescriptions for designing prototypes of instructional materials with different knowledge structures, the Elaboration Theory of Instruction (ETI) is based on important principles and theories of learning and instruction. This paper reports three experiments which tested ETI. The first experiment…

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System.

PubMed

Chen, Donghua; Zhang, Runtong; Liu, Kecheng; Hou, Lei

2018-06-19

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.

Ontology-based content analysis of US patent applications from 2001-2010.

PubMed

Weber, Lutz; Böhme, Timo; Irmer, Matthias

2013-01-01

Ontology-based semantic text analysis methods allow to automatically extract knowledge relationships and data from text documents. In this review, we have applied these technologies for the systematic analysis of pharmaceutical patents. Hierarchical concepts from the knowledge domains of chemical compounds, diseases and proteins were used to annotate full-text US patent applications that deal with pharmacological activities of chemical compounds and filed in the years 2001-2010. Compounds claimed in these applications have been classified into their respective compound classes to review the distribution of scaffold types or general compound classes such as natural products in a time-dependent manner. Similarly, the target proteins and claimed utility of the compounds have been classified and the most relevant were extracted. The method presented allows the discovery of the main areas of innovation as well as emerging fields of patenting activities - providing a broad statistical basis for competitor analysis and decision-making efforts.

Effect of age on variability in the production of text-based global inferences.

PubMed

Williams, Lynne J; Dunlop, Joseph P; Abdi, Hervé

2012-01-01

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.

MedTxting: Learning based and Knowledge Rich SMS-style Medical Text Contraction

PubMed Central

Liu, Feifan; Moosavinasab, Soheil; Houston, Thomas K.; Yu, Hong

2012-01-01

In mobile health (M-health), Short Message Service (SMS) has shown to improve disease related self-management and health service outcomes, leading to enhanced patient care. However, the hard limit on character size for each message limits the full value of exploring SMS communication in health care practices. To overcome this problem and improve the efficiency of clinical workflow, we developed an innovative system, MedTxting (available at http://medtxting.askhermes.org), which is a learning-based but knowledge-rich system that compresses medical texts in a SMS style. Evaluations on clinical questions and discharge summary narratives show that MedTxting can effectively compress medical texts with reasonable readability and noticeable size reduction. Findings in this work reveal potentials of MedTxting to the clinical settings, allowing for real-time and cost-effective communication, such as patient condition reporting, medication consulting, physicians connecting to share expertise to improve point of care. PMID:23304328

A computational model for simulating text comprehension.

PubMed

Lemaire, Benoît; Denhière, Guy; Bellissens, Cédrick; Jhean-Larose, Sandra

2006-11-01

In the present article, we outline the architecture of a computer program for simulating the process by which humans comprehend texts. The program is based on psycholinguistic theories about human memory and text comprehension processes, such as the construction-integration model (Kintsch, 1998), the latent semantic analysis theory of knowledge representation (Landauer & Dumais, 1997), and the predication algorithms (Kintsch, 2001; Lemaire & Bianco, 2003), and it is intended to help psycholinguists investigate the way humans comprehend texts.

ArdenML: The Arden Syntax Markup Language (or Arden Syntax: It's Not Just Text Any More!)

PubMed Central

Sailors, R. Matthew

2001-01-01

It is no longer necessary to think of Arden Syntax as simply a text-based knowledge base format. The development of ArdenML (Arden Syntax Markup Language), an XML-based markup language allows structured access to most of the maintenance and library categories without the need to write or buy a compiler may lead to the development of simple commercial and freeware tools for processing Arden Syntax Medical Logic Modules (MLMs)

Knowledge retrieval as one type of knowledge-based decision support in medicine: results of an evaluation study.

PubMed

Haux, R; Grothe, W; Runkel, M; Schackert, H K; Windeler, H J; Winter, A; Wirtz, R; Herfarth, C; Kunze, S

1996-04-01

We report on a prospective, prolective observational study, supplying information on how physicians and other health care professionals retrieve medical knowledge on-line within the Heidelberg University Hospital information system. Within this hospital information system, on-line access to medical knowledge has been realised by installing a medical knowledge server in the range of about 24 GB and by providing access to it by health care professional workstations in wards, physicians' rooms, etc. During the study, we observed about 96 accesses per working day. The main group of health care professionals retrieving medical knowledge were physicians and medical students. Primary reasons for its utilisation were identified as support for the users' scientific work (50%), own clinical cases (19%), general medical problems (14%) and current clinical problems (13%). Health care professionals had accesses to medical knowledge bases such as MEDLINE (79%), drug bases ('Rote Liste', 6%), and to electronic text books and knowledge base systems as well. Sixty-five percent of accesses to medical knowledge were judged to be successful. In our opinion, medical knowledge retrieval can serve as a first step towards knowledge processing in medicine. We point out the consequences for the management of hospital information systems in order to provide the prerequisites for such a type of knowledge retrieval.

Validation and Comprehension of Text Information: Two Sides of the Same Coin

ERIC Educational Resources Information Center

Richter, Tobias

2015-01-01

In psychological research, the comprehension of linguistic information and the knowledge-based assessment of its validity are often regarded as two separate stages of information processing. Recent findings in psycholinguistics and text comprehension research call this two-stage model into question. In particular, validation can affect…

Nontraditional Texts and the Struggling/Reluctant Reader

ERIC Educational Resources Information Center

Fingon, Joan C.

2012-01-01

This article highlights the Wimpy Kid diary book series by Jeff Kenney and discusses how educators can increase their knowledge base and take advantage of integrating such highly visual and nontraditional texts within the reading and language arts curriculum to enhance students' vocabulary development and reading comprehension. The article…

35 Strategies for Guiding Readers through Informational Texts

ERIC Educational Resources Information Center

Moss, Barbara; Loh, Virginia S.

2010-01-01

This practical guide presents inspiring, research-based activities for teaching students in grades K-12 how to read and think critically about informational texts. With five essential types of strategies, seasoned and preservice teachers learn ways to help students select engaging, challenging reading materials; develop their knowledge of history,…

Developmental, Component-Based Model of Reading Fluency: An Investigation of Predictors of Word-Reading Fluency, Text-Reading Fluency, and Reading Comprehension.

PubMed

Kim, Young-Suk Grace

2015-01-01

The primary goal was to expand our understanding of text reading fluency (efficiency or automaticity)-how its relation to other constructs (e.g., word reading fluency and reading comprehension) changes over time and how it is different from word reading fluency and reading comprehension. We examined (1) developmentally changing relations among word reading fluency, listening comprehension, text reading fluency, and reading comprehension; (2) the relation of reading comprehension to text reading fluency; (3) unique emergent literacy predictors (i.e., phonological awareness, orthographic awareness, morphological awareness, letter name knowledge, vocabulary) of text reading fluency vs. word reading fluency; and (4) unique language and cognitive predictors (e.g., vocabulary, grammatical knowledge, theory of mind) of text reading fluency vs. reading comprehension. These questions were addressed using longitudinal data (two timepoints; Mean age = 5;24 & 6;08) from Korean-speaking children ( N = 143). Results showed that listening comprehension was related to text reading fluency at time 2, but not at time 1. At both times text reading fluency was related to reading comprehension, and reading comprehension was related to text reading fluency over and above word reading fluency and listening comprehension. Orthographic awareness was related to text reading fluency over and above other emergent literacy skills and word reading fluency. Vocabulary and grammatical knowledge were independently related to text reading fluency and reading comprehension whereas theory of mind was related to reading comprehension, but not text reading fluency. These results reveal developmental nature of relations and mechanism of text reading fluency in reading development.

Developmental, Component-Based Model of Reading Fluency: An Investigation of Predictors of Word-Reading Fluency, Text-Reading Fluency, and Reading Comprehension

PubMed Central

Kim, Young-Suk Grace

2015-01-01

The primary goal was to expand our understanding of text reading fluency (efficiency or automaticity)—how its relation to other constructs (e.g., word reading fluency and reading comprehension) changes over time and how it is different from word reading fluency and reading comprehension. We examined (1) developmentally changing relations among word reading fluency, listening comprehension, text reading fluency, and reading comprehension; (2) the relation of reading comprehension to text reading fluency; (3) unique emergent literacy predictors (i.e., phonological awareness, orthographic awareness, morphological awareness, letter name knowledge, vocabulary) of text reading fluency vs. word reading fluency; and (4) unique language and cognitive predictors (e.g., vocabulary, grammatical knowledge, theory of mind) of text reading fluency vs. reading comprehension. These questions were addressed using longitudinal data (two timepoints; Mean age = 5;24 & 6;08) from Korean-speaking children (N = 143). Results showed that listening comprehension was related to text reading fluency at time 2, but not at time 1. At both times text reading fluency was related to reading comprehension, and reading comprehension was related to text reading fluency over and above word reading fluency and listening comprehension. Orthographic awareness was related to text reading fluency over and above other emergent literacy skills and word reading fluency. Vocabulary and grammatical knowledge were independently related to text reading fluency and reading comprehension whereas theory of mind was related to reading comprehension, but not text reading fluency. These results reveal developmental nature of relations and mechanism of text reading fluency in reading development. PMID:26435550

Does the Component Processes Task Assess Text-Based Inferences Important for Reading Comprehension? A Path Analysis in Primary School Children

PubMed Central

Wassenburg, Stephanie I.; de Koning, Björn B.; de Vries, Meinou H.; van der Schoot, Menno

2016-01-01

Using a component processes task (CPT) that differentiates between higher-level cognitive processes of reading comprehension provides important advantages over commonly used general reading comprehension assessments. The present study contributes to further development of the CPT by evaluating the relative contributions of its components (text memory, text inferencing, and knowledge integration) and working memory to general reading comprehension within a single study using path analyses. Participants were 173 third- and fourth-grade children. As hypothesized, knowledge integration was the only component of the CPT that directly contributed to reading comprehension, indicating that the text-inferencing component did not assess inferential processes related to reading comprehension. Working memory was a significant predictor of reading comprehension over and above the component processes. Future research should focus on finding ways to ensure that the text-inferencing component taps into processes important for reading comprehension. PMID:27378989

Augmenting Paper-Based Reading Activity with Direct Access to Digital Materials and Scaffolded Questioning

ERIC Educational Resources Information Center

Chen, Nian-Shing; Teng, Daniel Chia-En; Lee, Cheng-Han; Kinshuk

2011-01-01

Comprehension is the goal of reading. However, students often encounter reading difficulties due to the lack of background knowledge and proper reading strategy. Unfortunately, print text provides very limited assistance to one's reading comprehension through its static knowledge representations such as symbols, charts, and graphs. Integrating…

Writing for a Living: Literacy and the Knowledge Economy

ERIC Educational Resources Information Center

Brandt, Deborah

2005-01-01

This article seeks to explore the influence of the knowledge economy on the status of writing and literacy. It inquires into what happens to writers and their writing when texts serve as the chief commercial products of an organization--when such high-stakes factors as corporate reputation, client base, licensing, competitive advantage, growth,…

The Interaction of Knowledge and Text Structure on the Ability to Identify Main Ideas in Texts. Content Knowledge and Reading Comprehension.

ERIC Educational Resources Information Center

Day, Jeanne D.; Engelhardt, Jean

Two studies examined how the factors of content-relevant knowledge and text organization influence students' abilities to study and to remember text information. The first experiment examined the effect of prior content knowledge on students' ability to identify important information in the text. Forty 7th- and forty 11th-grade students, experts…

Document Exploration and Automatic Knowledge Extraction for Unstructured Biomedical Text

NASA Astrophysics Data System (ADS)

Chu, S.; Totaro, G.; Doshi, N.; Thapar, S.; Mattmann, C. A.; Ramirez, P.

2015-12-01

We describe our work on building a web-browser based document reader with built-in exploration tool and automatic concept extraction of medical entities for biomedical text. Vast amounts of biomedical information are offered in unstructured text form through scientific publications and R&D reports. Utilizing text mining can help us to mine information and extract relevant knowledge from a plethora of biomedical text. The ability to employ such technologies to aid researchers in coping with information overload is greatly desirable. In recent years, there has been an increased interest in automatic biomedical concept extraction [1, 2] and intelligent PDF reader tools with the ability to search on content and find related articles [3]. Such reader tools are typically desktop applications and are limited to specific platforms. Our goal is to provide researchers with a simple tool to aid them in finding, reading, and exploring documents. Thus, we propose a web-based document explorer, which we called Shangri-Docs, which combines a document reader with automatic concept extraction and highlighting of relevant terms. Shangri-Docsalso provides the ability to evaluate a wide variety of document formats (e.g. PDF, Words, PPT, text, etc.) and to exploit the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and private cataloged databases simultaneously. Shangri-Docsutilizes Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific symptoms, diseases, drugs, and anatomical sites, mentioned in the text. cTAKES was originally designed specially to extract information from clinical medical records. Our investigation leads us to extend the automatic knowledge extraction process of cTAKES for biomedical research domain by improving the ontology guided information extraction process. We will describe our experience and implementation of our system and share lessons learned from our development. We will also discuss ways in which this could be adapted to other science fields. [1] Funk et al., 2014. [2] Kang et al., 2014. [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org

Illuminate Knowledge Elements in Geoscience Literature

NASA Astrophysics Data System (ADS)

Ma, X.; Zheng, J. G.; Wang, H.; Fox, P. A.

2015-12-01

There are numerous dark data hidden in geoscience literature. Efficient retrieval and reuse of those data will greatly benefit geoscience researches of nowadays. Among the works of data rescue, a topic of interest is illuminating the knowledge framework, i.e. entities and relationships, embedded in documents. Entity recognition and linking have received extensive attention in news and social media analysis, as well as in bioinformatics. In the domain of geoscience, however, such works are limited. We will present our work on how to use knowledge bases on the Web, such as ontologies and vocabularies, to facilitate entity recognition and linking in geoscience literature. The work deploys an un-supervised collective inference approach [1] to link entity mentions in unstructured texts to a knowledge base, which leverages the meaningful information and structures in ontologies and vocabularies for similarity computation and entity ranking. Our work is still in the initial stage towards the detection of knowledge frameworks in literature, and we have been collecting geoscience ontologies and vocabularies in order to build a comprehensive geoscience knowledge base [2]. We hope the work will initiate new ideas and collaborations on dark data rescue, as well as on the synthesis of data and knowledge from geoscience literature. References: 1. Zheng, J., Howsmon, D., Zhang, B., Hahn, J., McGuinness, D.L., Hendler, J., and Ji, H. 2014. Entity linking for biomedical literature. In Proceedings of ACM 8th International Workshop on Data and Text Mining in Bioinformatics, Shanghai, China. 2. Ma, X. Zheng, J., 2015. Linking geoscience entity mentions to the Web of Data. ESIP 2015 Summer Meeting, Pacific Grove, CA.

Superior Gain in Knowledge by Podcasts Versus Text-Based Learning in Teaching Orthopedics: A Randomized Controlled Trial.

PubMed

Back, David Alexander; von Malotky, Jennifer; Sostmann, Kai; Hube, Robert; Peters, Harm; Hoff, Eike

Digital learning (e-learning) has become a firm part of surgical undergraduate education. However, there is still a lack in analyzing e-learning tools in experimental settings without potentially biasing curricular influences. This study should compare students' learning outcome with podcasts versus book texts under laboratory conditions in the field of orthopedics. Voluntary medical students were randomly assigned for learning either with a book chapter or a podcast about common orthopedic diseases in an isolated computer room. Before and after intervention, students answered multiple-choice tests and questionnaires about their attitudes and satisfaction. The study was conducted from November 2012 to February 2013. Totally, 130 students were included (55 text users and 75 podcast users, 52 males and 78 females). There was a significant increase in the overall knowledge for both groups (p < 0.001). Podcast users scored significantly better in the posttests (p < 0.021) and achieved a significantly higher gain of knowledge compared to text users (p < 0.001). The evaluation also showed a significantly higher approval of podcasts regarding comprehensibility, teaching efficacy, or fun learning with it (p < 0.05). Females gained significantly more knowledge by the use of texts than males did (p = 0.04), without any sex difference when using podcasts. This study showed a significantly higher gain of knowledge and higher satisfaction from learning with podcasts compared to book texts among students. Podcasts seem to be beneficial when teaching defined orthopedic topics to medical students. Sex plays an additional independent role in the impact of e-learning tools on students' learning outcome. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

For You and Your Baby (4YYB): Adapting the Centers for Disease Control and Prevention's Text4Baby Program for Saudi Arabia.

PubMed

Bahanshal, Soha; Coughlin, Steven; Liu, Benyuan

2017-02-28

Poor birth outcomes in the Kingdom of Saudi Arabia (KSA) have been found to be partially due to missed prenatal appointments as well as lack of knowledge of healthy pregnancy behaviors. The objectives are to summarize birth outcomes and the antenatal care system in KSA, summarize research related to the US Text4Baby mobile health program, and outline the development of an Arabic version of the Text4baby app, For You and Your Baby (4YYB). First, birth outcomes, health care access, and smartphone usage among Saudi Arabian women are reviewed. Next, the current evidence behind Text4Baby is described. Finally, a plan to develop and test 4YYB is proposed. In the plan, studies will need to be conducted to determine the effectiveness of 4YYB in educating pregnant Saudi women on healthy knowledge and behaviors. This will create an evidence base behind 4YYB before it is launched as a full-scale public health effort in KSA. The KSA offers public medical services but remaining challenges include poor birth outcomes and health care access barriers. An estimated 73% to 84% of Saudi women of child-bearing age use smartphone social media apps. A total of 13 published articles on Text4Baby were identified and reviewed. Due to design limitations, the studies provide only limited evidence about the effectiveness of the program in increasing healthy pregnancy knowledge and behaviors. To be useful for Saudi women, the educational messages in 4YYB will need to be translated from English to Arabic and tailored for cultural norms. Developing the 4YYB Arabic-language app for use by pregnant Saudi Arabian women based on Text4Baby is a viable approach, but a rigorous study design is needed to determine its effectiveness in improving healthy pregnancy knowledge and behaviors. ©Soha Bahanshal, Steven Coughlin, Benyuan Liu. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 28.02.2017.

Machine-aided indexing at NASA

NASA Technical Reports Server (NTRS)

Silvester, June P.; Genuardi, Michael T.; Klingbiel, Paul H.

1994-01-01

This report describes the NASA Lexical Dictionary (NLD), a machine-aided indexing system used online at the National Aeronautics and Space Administration's Center for AeroSpace Information (CASI). This system automatically suggests a set of candidate terms from NASA's controlled vocabulary for any designated natural language text input. The system is comprised of a text processor that is based on the computational, nonsyntactic analysis of input text and an extensive knowledge base that serves to recognize and translate text-extracted concepts. The functions of the various NLD system components are described in detail, and production and quality benefits resulting from the implementation of machine-aided indexing at CASI are discussed.

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

ERIC Educational Resources Information Center

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

Comparison of computer-assisted instruction (CAI) versus traditional textbook methods for training in abdominal examination (Japanese experience).

PubMed

Qayumi, A K; Kurihara, Y; Imai, M; Pachev, G; Seo, H; Hoshino, Y; Cheifetz, R; Matsuura, K; Momoi, M; Saleem, M; Lara-Guerra, H; Miki, Y; Kariya, Y

2004-10-01

This study aimed to compare the effects of computer-assisted, text-based and computer-and-text learning conditions on the performances of 3 groups of medical students in the pre-clinical years of their programme, taking into account their academic achievement to date. A fourth group of students served as a control (no-study) group. Participants were recruited from the pre-clinical years of the training programmes in 2 medical schools in Japan, Jichi Medical School near Tokyo and Kochi Medical School near Osaka. Participants were randomly assigned to 4 learning conditions and tested before and after the study on their knowledge of and skill in performing an abdominal examination, in a multiple-choice test and an objective structured clinical examination (OSCE), respectively. Information about performance in the programme was collected from school records and students were classified as average, good or excellent. Student and faculty evaluations of their experience in the study were explored by means of a short evaluation survey. Compared to the control group, all 3 study groups exhibited significant gains in performance on knowledge and performance measures. For the knowledge measure, the gains of the computer-assisted and computer-assisted plus text-based learning groups were significantly greater than the gains of the text-based learning group. The performances of the 3 groups did not differ on the OSCE measure. Analyses of gains by performance level revealed that high achieving students' learning was independent of study method. Lower achieving students performed better after using computer-based learning methods. The results suggest that computer-assisted learning methods will be of greater help to students who do not find the traditional methods effective. Explorations of the factors behind this are a matter for future research.

SWAN: An expert system with natural language interface for tactical air capability assessment

NASA Technical Reports Server (NTRS)

Simmons, Robert M.

1987-01-01

SWAN is an expert system and natural language interface for assessing the war fighting capability of Air Force units in Europe. The expert system is an object oriented knowledge based simulation with an alternate worlds facility for performing what-if excursions. Responses from the system take the form of generated text, tables, or graphs. The natural language interface is an expert system in its own right, with a knowledge base and rules which understand how to access external databases, models, or expert systems. The distinguishing feature of the Air Force expert system is its use of meta-knowledge to generate explanations in the frame and procedure based environment.

Of Texts AND Translations And Rhizomes: Postcolonial Anxieties and Deracinations and Knowledge Constructions

ERIC Educational Resources Information Center

Ramanathan, Vaidehi

2006-01-01

This article uncovers some problems involved in culling and translating non-western texts--written in other languages, at particular times, for specific audiences, and rooted in particular local milieus--before assembling them into academic arguments in English in the west. Based on my longterm, evolving endeavour regarding English- and…

Text2Floss: the feasibility and acceptability of a text messaging intervention to improve oral health behavior and knowledge.

PubMed

Hashemian, Tony S; Kritz-Silverstein, Donna; Baker, Ryan

2015-01-01

Text messaging is useful for promoting numerous health-related behaviors. The Text2Floss Study examines the feasibility and utility of a 7-day text messaging intervention to improve oral health knowledge and behavior in mothers of young children. Mothers were recruited from a private practice and a community clinic. Of 156 mothers enrolled, 129 randomized into text (n = 60) and control groups (n = 69) completed the trial. Participants in the text group received text messages for 7 days, asking about flossing and presenting oral health information. Oral health behaviors and knowledge were surveyed pre- and post-intervention. At baseline, there were no differences between text and control group mothers in knowledge and behaviors (P > 0.10). Post-intervention, text group mothers flossed more (P = 0.01), had higher total (P = 0.0006) and specific (P < 0.05) knowledge, and tried to improve their child's oral health behaviors (P = 0.03) and decrease their soda and sugary snacks (P = 0.05) more than control mothers. Text messages were accepted and perceived as useful. Mothers receiving text messages improved their own oral health behaviors and knowledge as well as their behaviors regarding their children's oral health. Text messaging represents a viable method to improve oral health behaviors and knowledge. Its high acceptance may make it useful for preventing oral disease. © 2014 American Association of Public Health Dentistry.

A perceptive method for handwritten text segmentation

NASA Astrophysics Data System (ADS)

Lemaitre, Aurélie; Camillerapp, Jean; Coüasnon, Bertrand

2011-01-01

This paper presents a new method to address the problem of handwritten text segmentation into text lines and words. Thus, we propose a method based on the cooperation among points of view that enables the localization of the text lines in a low resolution image, and then to associate the pixels at a higher level of resolution. Thanks to the combination of levels of vision, we can detect overlapping characters and re-segment the connected components during the analysis. Then, we propose a segmentation of lines into words based on the cooperation among digital data and symbolic knowledge. The digital data are obtained from distances inside a Delaunay graph, which gives a precise distance between connected components, at the pixel level. We introduce structural rules in order to take into account some generic knowledge about the organization of a text page. This cooperation among information gives a bigger power of expression and ensures the global coherence of the recognition. We validate this work using the metrics and the database proposed for the segmentation contest of ICDAR 2009. Thus, we show that our method obtains very interesting results, compared to the other methods of the literature. More precisely, we are able to deal with slope and curvature, overlapping text lines and varied kinds of writings, which are the main difficulties met by the other methods.

A METHODOLOGY FOR INTEGRATING IMAGES AND TEXT FOR OBJECT IDENTIFICATION

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

Paulson, Patrick R.; Hohimer, Ryan E.; Doucette, Peter J.

2006-02-13

Often text and imagery contain information that must be combined to solve a problem. One approach begins with transforming the raw text and imagery into a common structure that contains the critical information in a usable form. This paper presents an application in which the imagery of vehicles and the text from police reports were combined to demonstrate the power of data fusion to correctly identify the target vehicle--e.g., a red 2002 Ford truck identified in a police report--from a collection of diverse vehicle images. The imagery was abstracted into a common signature by first capturing the conceptual models ofmore » the imagery experts in software. Our system then (1) extracted fundamental features (e.g., wheel base, color), (2) made inferences about the information (e.g., it’s a red Ford) and then (3) translated the raw information into an abstract knowledge signature that was designed to both capture the important features and account for uncertainty. Likewise, the conceptual models of text analysis experts were instantiated into software that was used to generate an abstract knowledge signature that could be readily compared to the imagery knowledge signature. While this experiment primary focus was to demonstrate the power of text and imagery fusion for a specific example it also suggested several ways that text and geo-registered imagery could be combined to help solve other types of problems.« less

The Effectiveness of an Online Knowledge Map Instructional Presentation

ERIC Educational Resources Information Center

Foor, Jamie L.

2011-01-01

In this study, I investigated the effectiveness of the knowledge map (k-map) instructional strategy compared to a text-based presentation in an online environment. K-maps consist of node-link representations of concepts that together form the content of a topic or domain. The benefits of using k-maps are that concepts and ideas are represented as…

Content Familiarity and Gender-Neutral Texts in Foreign Language Reading Comprehension

ERIC Educational Resources Information Center

Jalilehvand, Maryam; Samuel, Moses

2014-01-01

Based on the schema theory, it has been found that the background knowledge of males and females differs. This difference in background knowledge can affect the students' reading comprehension. In Iran, although boys and girls study in different schools, they follow the same curricula and syllabuses in all the schools. The present article reports…

Knowledge based word-concept model estimation and refinement for biomedical text mining.

PubMed

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

A bioinformatics knowledge discovery in text application for grid computing

PubMed Central

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-01-01

Background A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. Methods The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. Results A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. Conclusion In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities. PMID:19534749

A bioinformatics knowledge discovery in text application for grid computing.

PubMed

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-06-16

A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities.

Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks.

PubMed

Wang, Chenguang; Song, Yangqiu; El-Kishky, Ahmed; Roth, Dan; Zhang, Ming; Han, Jiawei

2015-08-01

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, Word-Net. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.

Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks

PubMed Central

Wang, Chenguang; Song, Yangqiu; El-Kishky, Ahmed; Roth, Dan; Zhang, Ming; Han, Jiawei

2015-01-01

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, Word-Net. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features. PMID:26705504

Implementation of a Text-Based Content Intervention in Secondary Social Studies Classes.

PubMed

Wanzek, Jeanne; Vaughn, Sharon

2016-12-01

We describe teacher fidelity (adherence to the components of the treatment as specified by the research team) based on a series of studies of a multicomponent intervention, Promoting Acceleration of Comprehension and Content Through Text (PACT), with middle and high school social studies teachers and their students. Findings reveal that even with highly specified materials and implementing practices that are aligned with effective reading comprehension and content instruction, teachers' fidelity was consistently low for some components and high for others. Teachers demonstrated consistently high implementation fidelity and quality for the instructional components of building background knowledge (comprehension canopy) and teaching key content vocabulary (essential words), whereas we recorded consistently lower fidelity and quality of implementation for the instructional components of critical reading and knowledge application. © 2016 Wiley Periodicals, Inc.

Domain-independent information extraction in unstructured text

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

Irwin, N.H.

Extracting information from unstructured text has become an important research area in recent years due to the large amount of text now electronically available. This status report describes the findings and work done during the second year of a two-year Laboratory Directed Research and Development Project. Building on the first-year`s work of identifying important entities, this report details techniques used to group words into semantic categories and to output templates containing selective document content. Using word profiles and category clustering derived during a training run, the time-consuming knowledge-building task can be avoided. Though the output still lacks in completeness whenmore » compared to systems with domain-specific knowledge bases, the results do look promising. The two approaches are compatible and could complement each other within the same system. Domain-independent approaches retain appeal as a system that adapts and learns will soon outpace a system with any amount of a priori knowledge.« less

Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial.

PubMed

Buch, Steen Vigh; Treschow, Frederik Philip; Svendsen, Jesper Brink; Worm, Bjarne Skjødt

2014-01-01

This study investigated the effectiveness of two different levels of e-learning when teaching clinical skills to medical students. Sixty medical students were included and randomized into two comparable groups. The groups were given either a video- or text/picture-based e-learning module and subsequently underwent both theoretical and practical examination. A follow-up test was performed 1 month later. The students in the video group performed better than the illustrated text-based group in the practical examination, both in the primary test (P<0.001) and in the follow-up test (P<0.01). Regarding theoretical knowledge, no differences were found between the groups on the primary test, though the video group performed better on the follow-up test (P=0.04). Video-based e-learning is superior to illustrated text-based e-learning when teaching certain practical clinical skills.

Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial

PubMed Central

Buch, Steen Vigh; Treschow, Frederik Philip; Svendsen, Jesper Brink; Worm, Bjarne Skjødt

2014-01-01

Background and aims This study investigated the effectiveness of two different levels of e-learning when teaching clinical skills to medical students. Materials and methods Sixty medical students were included and randomized into two comparable groups. The groups were given either a video- or text/picture-based e-learning module and subsequently underwent both theoretical and practical examination. A follow-up test was performed 1 month later. Results The students in the video group performed better than the illustrated text-based group in the practical examination, both in the primary test (P<0.001) and in the follow-up test (P<0.01). Regarding theoretical knowledge, no differences were found between the groups on the primary test, though the video group performed better on the follow-up test (P=0.04). Conclusion Video-based e-learning is superior to illustrated text-based e-learning when teaching certain practical clinical skills. PMID:25152638

Building a glaucoma interaction network using a text mining approach.

PubMed

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

Effect of Age on Variability in the Production of Text-Based Global Inferences

PubMed Central

Williams, Lynne J.; Dunlop, Joseph P.; Abdi, Hervé

2012-01-01

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one’s world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation–a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging. PMID:22590523

Knowledge mobilization in healthcare organizations: a view from the resource-based view of the firm.

PubMed

Ferlie, Ewan; Crilly, Tessa; Jashapara, Ashok; Trenholm, Susan; Peckham, Anna; Currie, Graeme

2015-03-01

This short literature review argues that the Resource-Based View (RBV) school of strategic management has recently become of increased interest to scholars of healthcare organizations. RBV links well to the broader interest in more effective Knowledge Mobilization (KM) in healthcare. The paper outlines and discusses key concepts, texts and authors from the RBV tradition and gives recent examples of how RBV concepts have been applied fruitfully to healthcare settings. It concludes by setting out a future research agenda.

The Automatic Assessment of Free Text Answers Using a Modified BLEU Algorithm

ERIC Educational Resources Information Center

Noorbehbahani, F.; Kardan, A. A.

2011-01-01

e-Learning plays an undoubtedly important role in today's education and assessment is one of the most essential parts of any instruction-based learning process. Assessment is a common way to evaluate a student's knowledge regarding the concepts related to learning objectives. In this paper, a new method for assessing the free text answers of…

The Interplay between Text-Based Vocabulary Size and Reading Comprehension of Turkish EFL Learners

ERIC Educational Resources Information Center

Güngör, Fatih; Yayli, Demet

2016-01-01

Reading is an indispensable skill for learners who desire success throughout their academic lives, and vocabulary knowledge is a sine qua non companion of reading comprehension. Despite being inextricably related entities, very little has been written about the necessary vocabulary coverage to understand an expository text and its equivalent in…

Measurement of smaller colon polyp in CT colonography images using morphological image processing.

PubMed

Manjunath, K N; Siddalingaswamy, P C; Prabhu, G K

2017-11-01

Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text] min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved. The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

PubMed

Papanikolaou, Nikolas; Pafilis, Evangelos; Nikolaou, Stavros; Ouzounis, Christos A; Iliopoulos, Ioannis; Promponas, Vasilis J

2011-12-01

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. http://biotextquest.biol.ucy.ac.cy vprobon@ucy.ac.cy; iliopj@med.uoc.gr Supplementary data are available at Bioinformatics online.

Text Processing of Domain-Related Information for Individuals with High and Low Domain Knowledge.

ERIC Educational Resources Information Center

Spilich, George J.; And Others

1979-01-01

The way in which previously acquired knowledge affects the processing on new domain-related information was investigated. Text processing was studied in two groups differing in knowledge of the domain of baseball. A knowledge structure for the domain was constructed, and text propositions were classified. (SW)

Knowledge management in health: a systematic literature review.

PubMed

Rocha, Elyrose Sousa Brito; Nagliate, Patricia; Furlan, Claudia Elisangela Bis; Rocha, Kerson; Trevizan, Maria Auxiliadora; Mendes, Isabel Amélia Costa

2012-01-01

Knowledge has been used as a resource for intelligent and effective action planning in organizations. Interest in research on knowledge management processes has intensified in different areas. A systematic literature review was accomplished, based on the question: what are the contributions of Brazilian and international journal publications on knowledge management in health? The sample totaled 32 items that complied with the inclusion criteria. The results showed that 78% of journals that published on the theme are international, 77% of researchers work in higher education and 65% have a Ph.D. The texts gave rise to five thematic categories, mainly: development of knowledge management systems in health (37.5%), discussion of knowledge management application in health (28.1%) and nurses' function in knowledge management (18.7%).

Gene prioritization and clustering by multi-view text mining

PubMed Central

2010-01-01

Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

Medical Named Entity Recognition for Indonesian Language Using Word Representations

NASA Astrophysics Data System (ADS)

Rahman, Arief

2018-03-01

Nowadays, Named Entity Recognition (NER) system is used in medical texts to obtain important medical information, like diseases, symptoms, and drugs. While most NER systems are applied to formal medical texts, informal ones like those from social media (also called semi-formal texts) are starting to get recognition as a gold mine for medical information. We propose a theoretical Named Entity Recognition (NER) model for semi-formal medical texts in our medical knowledge management system by comparing two kinds of word representations: cluster-based word representation and distributed representation.

[Recommendations in neonatal resuscitation].

PubMed

2004-01-01

The recommendations for neonatal resuscitation are not always based on sufficient scientific evidence and thus expert consensus based on current research, knowledge, and experience are useful for formulating practical protocols that are easy to follow. The latest recommendations, in 2000, modified previously published recommendations and are included in the present text.

Bringing the Text to Life and into Our Lives: Jewish Education and the Arts

ERIC Educational Resources Information Center

Backenroth, Ofra; Epstein, Shira D.; Miller, Helena

2006-01-01

This article explores how arts-based learning can facilitate understandings of Jewish religious texts. Through practical examples drawn from our own research, from the worlds of dance, drama, and the visual arts in education, we demonstrate the ways in which arts can allow for the transmission of information and knowledge, as well as offer a…

The Influence of Narrative and Expository Lesson Text Structures on Knowledge Structures: Alternate Measures of Knowledge Structure

ERIC Educational Resources Information Center

Clariana, Roy B.; Wolfe, Michael B.; Kim, Kyung

2014-01-01

This investigation applies two approaches for representing and comparing text structures as undirected network graphs to describe the influence of narrative and expository lesson texts on readers' knowledge structure elicited as free recall. Narrative and expository lesson texts and undergraduate participants' free recall essays (n = 90)…

Approaching Etuaptmumk--introducing a consensus-based mixed method for health services research.

PubMed

Chatwood, Susan; Paulette, Francois; Baker, Ross; Eriksen, Astrid; Hansen, Ketil Lenert; Eriksen, Heidi; Hiratsuka, Vanessa; Lavoie, Josée; Lou, Wendy; Mauro, Ian; Orbinski, James; Pabrum, Nathalie; Retallack, Hanna; Brown, Adalsteinn

2015-01-01

With the recognized need for health systems' improvements in the circumpolar and indigenous context, there has been a call to expand the research agenda across all sectors influencing wellness and to recognize academic and indigenous knowledge through the research process. Despite being recognized as a distinct body of knowledge in international forums and across indigenous groups, examples of methods and theories based on indigenous knowledge are not well documented in academic texts or peer-reviewed literature on health systems. This paper describes the use of a consensus-based, mixed method with indigenous knowledge by an experienced group of researchers and indigenous knowledge holders who collaborated on a study that explored indigenous values underlying health systems stewardship. The method is built on the principles of Etuaptmumk or two-eyed seeing, which aim to respond to and resolve the inherent conflicts between indigenous ways of knowing and the scientific inquiry that informs the evidence base in health care. Mixed methods' frameworks appear to provide a framing suitable for research questions that require data from indigenous knowledge sources and western knowledge. The nominal consensus method, as a western paradigm, was found to be responsive to embedding of indigenous knowledge and allowed space to express multiple perspectives and reach consensus on the question at hand. Further utilization and critical evaluation of this mixed methodology with indigenous knowledge are required.

Approaching Etuaptmumk – introducing a consensus-based mixed method for health services research

PubMed Central

Chatwood, Susan; Paulette, Francois; Baker, Ross; Eriksen, Astrid; Hansen, Ketil Lenert; Eriksen, Heidi; Hiratsuka, Vanessa; Lavoie, Josée; Lou, Wendy; Mauro, Ian; Orbinski, James; Pabrum, Nathalie; Retallack, Hanna; Brown, Adalsteinn

2015-01-01

With the recognized need for health systems’ improvements in the circumpolar and indigenous context, there has been a call to expand the research agenda across all sectors influencing wellness and to recognize academic and indigenous knowledge through the research process. Despite being recognized as a distinct body of knowledge in international forums and across indigenous groups, examples of methods and theories based on indigenous knowledge are not well documented in academic texts or peer-reviewed literature on health systems. This paper describes the use of a consensus-based, mixed method with indigenous knowledge by an experienced group of researchers and indigenous knowledge holders who collaborated on a study that explored indigenous values underlying health systems stewardship. The method is built on the principles of Etuaptmumk or two-eyed seeing, which aim to respond to and resolve the inherent conflicts between indigenous ways of knowing and the scientific inquiry that informs the evidence base in health care. Mixed methods’ frameworks appear to provide a framing suitable for research questions that require data from indigenous knowledge sources and western knowledge. The nominal consensus method, as a western paradigm, was found to be responsive to embedding of indigenous knowledge and allowed space to express multiple perspectives and reach consensus on the question at hand. Further utilization and critical evaluation of this mixed methodology with indigenous knowledge are required. PMID:26004427

Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways.

PubMed

Willemsen, A M; Jansen, G A; Komen, J C; van Hooff, S; Waterham, H R; Brites, P M T; Wanders, R J A; van Kampen, A H C

2008-08-15

One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts' minds and may be highly specific, heterogeneous, complex and voluminous. We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information. The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support-->Web applications). Supplementary data is available from www.bioinformaticslaboratory.nl (Research-->Output--> Publications--> KB_SuppInfo)

Research on a Frame-Based Model of Reading Comprehension. Final Report.

ERIC Educational Resources Information Center

Goldstein, Ira

This report summarizes computational investigations of language comprehension based on Marvin Minsky's theory of frames, a recent advance in artifical intelligence theories about the representation of knowledge. The investigations discussed explored frame theory as a basis for text comprehension by implementing models of the theory and developing…

Is an Illustration Always Worth Ten Thousand Words? Effects of Prior Knowledge, Learning Style and Multimedia Illustrations on Text Comprehension.

ERIC Educational Resources Information Center

Ollerenshaw, Alison; Aidman, Eugene; Kidd, Garry

1997-01-01

This study examined comprehension in four groups of undergraduates under text only, multimedia, and two diagram conditions of text supplementation. Results indicated that effects of text supplementation are mediated by prior knowledge and learning style: multimedia appears more beneficial to surface learners with little prior knowledge and makes…

Figure-associated text summarization and evaluation.

PubMed

Polepalli Ramesh, Balaji; Sethi, Ricky J; Yu, Hong

2015-01-01

Biomedical literature incorporates millions of figures, which are a rich and important knowledge resource for biomedical researchers. Scientists need access to the figures and the knowledge they represent in order to validate research findings and to generate new hypotheses. By themselves, these figures are nearly always incomprehensible to both humans and machines and their associated texts are therefore essential for full comprehension. The associated text of a figure, however, is scattered throughout its full-text article and contains redundant information content. In this paper, we report the continued development and evaluation of several figure summarization systems, the FigSum+ systems, that automatically identify associated texts, remove redundant information, and generate a text summary for every figure in an article. Using a set of 94 annotated figures selected from 19 different journals, we conducted an intrinsic evaluation of FigSum+. We evaluate the performance by precision, recall, F1, and ROUGE scores. The best FigSum+ system is based on an unsupervised method, achieving F1 score of 0.66 and ROUGE-1 score of 0.97. The annotated data is available at figshare.com (http://figshare.com/articles/Figure_Associated_Text_Summarization_and_Evaluation/858903).

Figure-Associated Text Summarization and Evaluation

PubMed Central

Polepalli Ramesh, Balaji; Sethi, Ricky J.; Yu, Hong

2015-01-01

Biomedical literature incorporates millions of figures, which are a rich and important knowledge resource for biomedical researchers. Scientists need access to the figures and the knowledge they represent in order to validate research findings and to generate new hypotheses. By themselves, these figures are nearly always incomprehensible to both humans and machines and their associated texts are therefore essential for full comprehension. The associated text of a figure, however, is scattered throughout its full-text article and contains redundant information content. In this paper, we report the continued development and evaluation of several figure summarization systems, the FigSum+ systems, that automatically identify associated texts, remove redundant information, and generate a text summary for every figure in an article. Using a set of 94 annotated figures selected from 19 different journals, we conducted an intrinsic evaluation of FigSum+. We evaluate the performance by precision, recall, F1, and ROUGE scores. The best FigSum+ system is based on an unsupervised method, achieving F1 score of 0.66 and ROUGE-1 score of 0.97. The annotated data is available at figshare.com (http://figshare.com/articles/Figure_Associated_Text_Summarization_and_Evaluation/858903). PMID:25643357

Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

ERIC Educational Resources Information Center

Mohler, Michael A. G.

2012-01-01

In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating…

Semantic Relatedness for Evaluation of Course Equivalencies

ERIC Educational Resources Information Center

Yang, Beibei

2012-01-01

Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the…

Learning second language vocabulary: neural dissociation of situation-based learning and text-based learning.

PubMed

Jeong, Hyeonjeong; Sugiura, Motoaki; Sassa, Yuko; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

2010-04-01

Second language (L2) acquisition necessitates learning and retrieving new words in different modes. In this study, we attempted to investigate the cortical representation of an L2 vocabulary acquired in different learning modes and in cross-modal transfer between learning and retrieval. Healthy participants learned new L2 words either by written translations (text-based learning) or in real-life situations (situation-based learning). Brain activity was then measured during subsequent retrieval of these words. The right supramarginal gyrus and left middle frontal gyrus were involved in situation-based learning and text-based learning, respectively, whereas the left inferior frontal gyrus was activated when learners used L2 knowledge in a mode different from the learning mode. Our findings indicate that the brain regions that mediate L2 memory differ according to how L2 words are learned and used. Copyright 2009 Elsevier Inc. All rights reserved.

A Knowledge-Based Approach to Language Production

DTIC Science & Technology

1985-08-01

representation -- the internal structures which the system is generating from. and (3) the choice problem -- how the intricate relationship between the...texts from several different internal representations. One example of the output produced by MUMBLE is the following text, produced from a...brackets. This notation is used to illustrate some templates which are encoded in Ace. The internal representation of these templates is achieved using

Knowledge Based Text Generation

DTIC Science & Technology

1989-08-01

Number 4, October-December, 1985, pp. 219-242. de Joia , A. and Stenton, A., Terms in Linguistics: A Guide to Halliday, London: Batsford Academic and...extraction of text schemata and their corresponding rhetorical predicates; design of a system motivated by the desire for domain and language independence...semantics and semantics effects syntax. Functional Linguistic Framework Page 19 The design of GENNY was guided by the functional paradigm. Provided a

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

PubMed Central

Zheng, Bin; McLean, David C; Lu, Xinghua

2006-01-01

Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text. PMID:16466569

The future is written: impact of scripts on the cognition, selection, knowledge and transmission of medicinal plant use and its implications for ethnobotany and ethnopharmacology.

PubMed

Leonti, Marco

2011-04-12

Apart from empirically learned medicinal and pharmacological properties, the selection of medicinal plants is dependent on cognitive features, ecological factors and cultural history. In literate societies the transmission of medicinal plant knowledge through texts and, more recently, other media containing local as well as non-local knowledge has a more immediate and a more prolonged effect than oral transmission. Therefore, I try to visualize how field based studies in ethnobiology and especially medical ethnobotany and ethnopharmacology run the risk of repeating information and knowledge and illustrate the importance of differentiating and acknowledging the origin, transmission and rationale of plant use made by humans. Reviewing literature dealing with the traditional parameters (e.g. hot/cold dichotomy, organoleptic properties, doctrine of signatures) influencing the selection and transmission of plant use in a juxtaposition to our recent finding of causal influence of text on local plant use. Discussing the passing down of knowledge by text as a special case of oblique/one-to-many knowledge transmission. Historical texts on materia medica, popular books on plant use, clinical studies, and informants of ethnobotanical field studies generate a circle of information and knowledge, which progressively conditions the results of ethnobotanical field studies. While text reporting on phytotherapeutical trends may cause innovation through the introduction of "new" applications to local customs, persistently repeating well established folk remedies leads to the consolidation of such uses adding a conservative dimension to a local pharmacopoeia, which might not actually be there to that extent. Such a "shaping" of what might appear to be the results of a field investigation is clearly outside the ordinary principles of scientific enquiry. The traditional pillars of ethnobotanical field studies - that is, "input to drug discovery" and "conservation of cultural heritage" - are also incompatible with this process. Ethnobotancial field studies aimed at a contribution to natural products research and/or the conservation of cultural heritage, as well as those aimed at an assessment and validation of local pharmacopoeias should differentiate between local plant use and widespread as well as modern knowledge reported in popular textbooks and scientific literature. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Information Gain Based Dimensionality Selection for Classifying Text Documents

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

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Supporting Students' Learning in the Domain of Computer Science

ERIC Educational Resources Information Center

Gasparinatou, Alexandra; Grigoriadou, Maria

2011-01-01

Previous studies have shown that students with low knowledge understand and learn better from more cohesive texts, whereas high-knowledge students have been shown to learn better from texts of lower cohesion. This study examines whether high-knowledge readers in computer science benefit from a text of low cohesion. Undergraduate students (n = 65)…

Educating Students with Autism Spectrum Disorders: Research-Based Principles and Practices

ERIC Educational Resources Information Center

Zager, Dianne, Ed.; Wehmeyer, Michael L., Ed.; Simpson, Richard L., Ed.

2011-01-01

Similar to a handbook in its comprehensive description of the theory and research supporting current practices in the treatment of autism spectrum disorders, this interdisciplinary text shows how the existing knowledge base can be used to explore promising new possibilities related to the field's many unanswered questions. This book is appropriate…

RC-MAPS: Bridging the Comprehension Gap in EAP Reading

ERIC Educational Resources Information Center

Sterzik, Angela Meyer; Fraser, Carol

2012-01-01

In academic environments, reading is assigned not simply to transmit information; students are required to take the information, and based on the task set by the instructor, assess, analyze, and critique it on the basis of personal experiences, prior knowledge, and other readings (Grabe, 2009). Thus text-based comprehension (Kintsch, 1998) alone…

Processing and memory of information presented in narrative or expository texts.

PubMed

Wolfe, Michael B W; Woodwyk, Joshua M

2010-09-01

Previous research suggests that narrative and expository texts differ in the extent to which they prompt students to integrate to-be-learned content with relevant prior knowledge during comprehension. We expand on previous research by examining on-line processing and representation in memory of to-be-learned content that is embedded in narrative or expository texts. We are particularly interested in how differences in the use of relevant prior knowledge leads to differences in terms of levels of discourse representation (textbase vs. situation model). A total of 61 university undergraduates in Expt 1, and 160 in Expt 2. In Expt 1, subjects thought out loud while comprehending circulatory system content embedded in a narrative or expository text, followed by free recall of text content. In Expt 2, subjects read silently and completed a sentence recognition task to assess memory. In Expt 1, subjects made more associations to prior knowledge while reading the expository text, and recalled more content. Content recall was also correlated with amount of relevant prior knowledge for subjects who read the expository text but not the narrative text. In Expt 2, subjects reading the expository text (compared to the narrative text) had a weaker textbase representation of the to-be-learned content, but a marginally stronger situation model. Results suggest that in terms of to-be-learned content, expository texts trigger students to utilize relevant prior knowledge more than narrative texts.

ECO: A Framework for Entity Co-Occurrence Exploration with Faceted Navigation

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

Halliday, K. D.

2010-08-20

Even as highly structured databases and semantic knowledge bases become more prevalent, a substantial amount of human knowledge is reported as written prose. Typical textual reports, such as news articles, contain information about entities (people, organizations, and locations) and their relationships. Automatically extracting such relationships from large text corpora is a key component of corporate and government knowledge bases. The primary goal of the ECO project is to develop a scalable framework for extracting and presenting these relationships for exploration using an easily navigable faceted user interface. ECO uses entity co-occurrence relationships to identify related entities. The system aggregates andmore » indexes information on each entity pair, allowing the user to rapidly discover and mine relational information.« less

Knowledge-based control of an adaptive interface

NASA Technical Reports Server (NTRS)

Lachman, Roy

1989-01-01

The analysis, development strategy, and preliminary design for an intelligent, adaptive interface is reported. The design philosophy couples knowledge-based system technology with standard human factors approaches to interface development for computer workstations. An expert system has been designed to drive the interface for application software. The intelligent interface will be linked to application packages, one at a time, that are planned for multiple-application workstations aboard Space Station Freedom. Current requirements call for most Space Station activities to be conducted at the workstation consoles. One set of activities will consist of standard data management services (DMS). DMS software includes text processing, spreadsheets, data base management, etc. Text processing was selected for the first intelligent interface prototype because text-processing software can be developed initially as fully functional but limited with a small set of commands. The program's complexity then can be increased incrementally. The intelligent interface includes the operator's behavior and three types of instructions to the underlying application software are included in the rule base. A conventional expert-system inference engine searches the data base for antecedents to rules and sends the consequents of fired rules as commands to the underlying software. Plans for putting the expert system on top of a second application, a database management system, will be carried out following behavioral research on the first application. The intelligent interface design is suitable for use with ground-based workstations now common in government, industrial, and educational organizations.

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records.

PubMed

Goldstein, Ayelet; Shahar, Yuval

2015-01-01

Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating summaries in two very different domains - Diabetes Management and Cardiothoracic surgery. In particular, we explain the process of generating a discharge summary of a patient who had undergone a Coronary Artery Bypass Graft operation, and a brief summary of the treatment of a diabetes patient for five years.

Query-oriented evidence extraction to support evidence-based medicine practice.

PubMed

Sarker, Abeed; Mollá, Diego; Paris, Cecile

2016-02-01

Evidence-based medicine practice requires medical practitioners to rely on the best available evidence, in addition to their expertise, when making clinical decisions. The medical domain boasts a large amount of published medical research data, indexed in various medical databases such as MEDLINE. As the size of this data grows, practitioners increasingly face the problem of information overload, and past research has established the time-associated obstacles faced by evidence-based medicine practitioners. In this paper, we focus on the problem of automatic text summarisation to help practitioners quickly find query-focused information from relevant documents. We utilise an annotated corpus that is specialised for the task of evidence-based summarisation of text. In contrast to past summarisation approaches, which mostly rely on surface level features to identify salient pieces of texts that form the summaries, our approach focuses on the use of corpus-based statistics, and domain-specific lexical knowledge for the identification of summary contents. We also apply a target-sentence-specific summarisation technique that reduces the problem of underfitting that persists in generic summarisation models. In automatic evaluations run over a large number of annotated summaries, our extractive summarisation technique statistically outperforms various baseline and benchmark summarisation models with a percentile rank of 96.8%. A manual evaluation shows that our extractive summarisation approach is capable of selecting content with high recall and precision, and may thus be used to generate bottom-line answers to practitioners' queries. Our research shows that the incorporation of specialised data and domain-specific knowledge can significantly improve text summarisation performance in the medical domain. Due to the vast amounts of medical text available, and the high growth of this form of data, we suspect that such summarisation techniques will address the time-related obstacles associated with evidence-based medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base.

PubMed

Lossio-Ventura, Juan Antonio; Hogan, William; Modave, François; Guo, Yi; He, Zhe; Hicks, Amanda; Bian, Jiang

2017-11-01

Obesity has been linked to several types of cancer. Access to adequate health information activates people's participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and deliver quality health information. Currently, there are several efforts in the biomedical domain to convert unstructured data to structured data and store them in Semantic Web knowledge bases (KB). In this demo paper, we present, OC-2-KB (Obesity and Cancer to Knowledge Base), a system that is tailored to guide the automatic KB construction for managing obesity and cancer knowledge from free-text scientific literature (i.e., PubMed abstracts) in a systematic way. OC-2-KB has two important modules which perform the acquisition of entities and the extraction then classification of relationships among these entities. We tested the OC-2-KB system on a data set with 23 manually annotated obesity and cancer PubMed abstracts and created a preliminary KB with 765 triples. We conducted a preliminary evaluation on this sample of triples and reported our evaluation results.

Making Sense of the ECG - Cases for Self-Assessment Houghton Andrew R Gray David Making Sense of the ECG - Cases for Self-Assessment 290pp Hodder Education 9780340946893 034094689X [Formula: see text].

PubMed

2010-10-27

This practical, pocket-book approach to ECG interpretation accompanies the well-known text Making Sense of the ECG, by the same authors. It is also designed to be used alone to test knowledge of ECG interpretation and to make clinical decisions based on presented scenarios.

Investigating the Effects of Background Knowledge on Chinese Word Processing during Text Reading: Evidence from Eye Movements

ERIC Educational Resources Information Center

Jian, Yu-Cin; Ko, Hwa-Wei

2014-01-01

This study investigates the effects of background knowledge on Chinese word processing during silent reading by monitoring adult readers' eye movements. Both higher knowledge (physics major) and lower knowledge (nonphysics major) graduate students were given physics texts to read. Higher knowledge readers spent less time rereading and had…

Using diagrams versus text for spaced restudy: Effects on learning in 10th grade biology classes.

PubMed

Bergey, Bradley W; Cromley, Jennifer G; Kirchgessner, Mandy L; Newcombe, Nora S

2015-03-01

Spaced restudy has been typically tested with written learning materials, but restudy with visual representations in actual classrooms is under-researched. We compared the effects of two spaced restudy interventions: A Diagram-Based Restudy (DBR) warm-up condition and a business-as-usual Text-Based Restudy (TBR) warm-up condition. One hundred and twenty-eight consented high school students in 15 classes. Students completed daily warm-ups over a 4-week period. Students were randomly assigned to conditions within classrooms. Warm-ups were independently completed at the start of class meetings and consisted of questions about content covered 1-10 days prior to each warm-up. Students received feedback on their answers each week. A series of ANOVAs and ANCOVAs was conducted. Results showed equal and significant growth from pre- to post-test for both conditions (d = .31-.67) on three outcomes: Biology knowledge, biology diagram comprehension (near transfer), and geology diagram comprehension (far transfer). ANCOVA results suggested that the magnitude of this increase was linked to the number of questions attempted during the intervention. For the DBR condition only, there were interactions with content knowledge on diagram comprehension gain scores and interactions with spatial scores on biology knowledge gain scores. Students with lower biology knowledge and lower Paper Folding Test scores were disadvantaged in the DBR condition, whereas the TBR condition was equitable across all levels of knowledge and spatial ability. © 2014 The British Psychological Society.

Text-Content-Analysis based on the Syntactic Correlations between Ontologies

NASA Astrophysics Data System (ADS)

Tenschert, Axel; Kotsiopoulos, Ioannis; Koller, Bastian

The work presented in this chapter is concerned with the analysis of semantic knowledge structures, represented in the form of Ontologies, through which Service Level Agreements (SLAs) are enriched with new semantic data. The objective of the enrichment process is to enable SLA negotiation in a way that is much more convenient for a Service Users. For this purpose the deployment of an SLA-Management-System as well as the development of an analyzing procedure for Ontologies is required. This chapter will refer to the BREIN, the FinGrid and the LarKC projects. The analyzing procedure examines the syntactic correlations of several Ontologies whose focus lies in the field of mechanical engineering. A method of analyzing text and content is developed as part of this procedure. In order to so, we introduce a formalism as well as a method for understanding content. The analysis and methods are integrated to an SLA Management System which enables a Service User to interact with the system as a service by negotiating the user requests and including the semantic knowledge. Through negotiation between Service User and Service Provider the analysis procedure considers the user requests by extending the SLAs with semantic knowledge. Through this the economic use of an SLA-Management-System is increased by the enhancement of SLAs with semantic knowledge structures. The main focus of this chapter is the analyzing procedure, respectively the Text-Content-Analysis, which provides the mentioned semantic knowledge structures.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Interactions among Elaborative Interrogation, Knowledge, and Interest in the Process of Constructing Knowledge from Text

ERIC Educational Resources Information Center

Ozgungor, Sevgi; Guthrie, John T.

2004-01-01

The authors examined the impact of elaborative interrogation on knowledge construction during expository text reading, specifically, the interactions among elaborative interrogation, knowledge, and interest. Three measures of learning were taken: recall, inference, and coherence. Elaborative interrogation affected all aspects of learning measured,…

Towards an Age-Phenome Knowledge-base

PubMed Central

2011-01-01

Background Currently, data about age-phenotype associations are not systematically organized and cannot be studied methodically. Searching for scientific articles describing phenotypic changes reported as occurring at a given age is not possible for most ages. Results Here we present the Age-Phenome Knowledge-base (APK), in which knowledge about age-related phenotypic patterns and events can be modeled and stored for retrieval. The APK contains evidence connecting specific ages or age groups with phenotypes, such as disease and clinical traits. Using a simple text mining tool developed for this purpose, we extracted instances of age-phenotype associations from journal abstracts related to non-insulin-dependent Diabetes Mellitus. In addition, links between age and phenotype were extracted from clinical data obtained from the NHANES III survey. The knowledge stored in the APK is made available for the relevant research community in the form of 'Age-Cards', each card holds the collection of all the information stored in the APK about a particular age. These Age-Cards are presented in a wiki, allowing community review, amendment and contribution of additional information. In addition to the wiki interaction, complex searches can also be conducted which require the user to have some knowledge of database query construction. Conclusions The combination of a knowledge model based repository with community participation in the evolution and refinement of the knowledge-base makes the APK a useful and valuable environment for collecting and curating existing knowledge of the connections between age and phenotypes. PMID:21651792

A collaborative filtering-based approach to biomedical knowledge discovery.

PubMed

Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

2018-02-15

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Information system to manage anatomical knowledge and image data about brain

NASA Astrophysics Data System (ADS)

Barillot, Christian; Gibaud, Bernard; Montabord, E.; Garlatti, S.; Gauthier, N.; Kanellos, I.

1994-09-01

This paper reports about first results obtained in a project aiming at developing a computerized system to manage knowledge about brain anatomy. The emphasis is put on the design of a knowledge base which includes a symbolic model of cerebral anatomical structures (grey nuclei, cortical structures such as gyri and sulci, verntricles, vessels, etc.) and of hypermedia facilities allowing to retrieve and display information associated with the objects (texts, drawings, images). Atlas plates digitized from a stereotactic atlas are also used to provide natural and effective communication means between the user and the system.

Automatic Domain Adaptation of Word Sense Disambiguation Based on Sublanguage Semantic Schemata Applied to Clinical Narrative

ERIC Educational Resources Information Center

Patterson, Olga

2012-01-01

Domain adaptation of natural language processing systems is challenging because it requires human expertise. While manual effort is effective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and confidentiality restrictions that hinder the…

Ubiquitous Discussion Forum: Introducing Mobile Phones and Voice Discussion into a Web Discussion Forum

ERIC Educational Resources Information Center

Wei, Fu-Hsiang; Chen, Gwo-Dong; Wang, Chin-Yeh; Li, Liang-Yi

2007-01-01

Web-based discussion forums enable users to share knowledge in straightforward and popular platforms. However, discussion forums have several problems, such as the lack of immediate delivery and response, the heavily text-based medium, inability to hear expressions of voice and the heuristically created discussion topics which can impede the…

The Impact of Project-Based Learning on Fourth-Grade Students' Understanding in Reading

ERIC Educational Resources Information Center

Williams, Dana L.

2017-01-01

The purpose of this quantitative, non-experimental, descriptive study was to determine if participation in project-based learning developed the understanding students need to transfer their knowledge and skills to achieve higher composite and reading scores, as well as demonstrate the ability to read increasingly complex texts on the ACT Aspire as…

Older adults' use of metacognitive knowledge in source monitoring: spared monitoring but impaired control.

PubMed

Kuhlmann, Beatrice G; Touron, Dayna R

2011-03-01

While episodic memory declines with age, metacognitive monitoring is spared. The current study explored whether older adults can use their preserved metacognitive knowledge to make source guesses in the absence of source memory. Through repetition, words from two sources (italic vs. bold text type) differed in memorability. There were no age differences in monitoring this difference despite an age difference in memory. Older adults used their metacognitive knowledge to make source guesses but showed a deficit in varying their source guessing based on word recognition. Therefore, older adults may not fully benefit from metacognitive knowledge about sources in source monitoring. (c) 2011 APA, all rights reserved.

Chemical name extraction based on automatic training data generation and rich feature set.

PubMed

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Txt Msg N School Literacy: Does Texting and Knowledge of Text Abbreviations Adversely Affect Children's Literacy Attainment?

ERIC Educational Resources Information Center

Plester, Beverly; Wood, Clare; Bell, Victoria

2008-01-01

This paper reports on two studies which investigated the relationship between children's texting behaviour, their knowledge of text abbreviations and their school attainment in written language skills. In Study One, 11-12-year-old children provided information on their texting behaviour. They were also asked to translate a standard English…

Text Difficulty Affects Metacomprehension Accuracy and Knowledge Test Performance in Text Learning

ERIC Educational Resources Information Center

Vössing, J.; Stamov-Roßnagel, C.; Heinitz, K.

2017-01-01

Metacomprehension as reflected in judgements of one's learning is crucial for self-regulated study, yet their accuracy is often low. We investigated text difficulty as a constraint on metacomprehension accuracy in text learning. A total of 235 participants studied a 10-section expository text and afterwards took a knowledge test. They made…

Understanding natural language for spacecraft sequencing

NASA Technical Reports Server (NTRS)

Katz, Boris; Brooks, Robert N., Jr.

1987-01-01

The paper describes a natural language understanding system, START, that translates English text into a knowledge base. The understanding and the generating modules of START share a Grammar which is built upon reversible transformations. Users can retrieve information by querying the knowledge base in English; the system then produces an English response. START can be easily adapted to many different domains. One such domain is spacecraft sequencing. A high-level overview of sequencing as it is practiced at JPL is presented in the paper, and three areas within this activity are identified for potential application of the START system. Examples are given of an actual dialog with START based on simulated data for the Mars Observer mission.

Extracting BI-RADS Features from Portuguese Clinical Texts.

PubMed

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2012-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser's performance is comparable to the manual method.

The Galileo PPS expert monitoring and diagnostic prototype

NASA Technical Reports Server (NTRS)

Bahrami, Khosrow

1989-01-01

The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text.

A multimedia Anatomy Browser incorporating a knowledge base and 3D images.

PubMed Central

Eno, K.; Sundsten, J. W.; Brinkley, J. F.

1991-01-01

We describe a multimedia program for teaching anatomy. The program, called the Anatomy Browser, displays cross-sectional and topographical images, with outlines around structures and regions of interest. The user may point to these structures and retrieve text descriptions, view symbolic relationships between structures, or view spatial relationships by accessing 3-D graphics animations from videodiscs produced specifically for this program. The software also helps students exercise what they have learned by asking them to identify structures by name and location. The program is implemented in a client-server architecture, with the user interface residing on a Macintosh, while images, data, and a growing symbolic knowledge base of anatomy are stored on a fileserver. This architecture allows us to develop practical tutorial modules that are in current use, while at the same time developing the knowledge base that will lead to more intelligent tutorial systems. PMID:1807699

Script-independent text line segmentation in freestyle handwritten documents.

PubMed

Li, Yi; Zheng, Yefeng; Doermann, David; Jaeger, Stefan; Li, Yi

2008-08-01

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike connected component based methods ( [1], [2] for example), the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [1]-[3]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise.

Exploring the Relationship between Children's Knowledge of Text Message Abbreviations and School Literacy Outcomes

ERIC Educational Resources Information Center

Plester, Beverly; Wood, Clare; Joshi, Puja

2009-01-01

This paper presents a study of 88 British 10-12-year-old children's knowledge of text message (SMS) abbreviations ("textisms") and how it relates to their school literacy attainment. As a measure of textism knowledge, the children were asked to compose text messages they might write if they were in each of a set of scenarios. Their text…

Teaching Writing

ERIC Educational Resources Information Center

Tomas, Z.; Kostka, I.; Mott-Smith, J. A.

2013-01-01

The authors of "Teaching Writing" draw on their years of teaching and their knowledge of theory and research to present major concepts in teaching L2 writing. These concepts encompass how cultural differences affect the writing class, planning instruction, text-based writing, writing strategies, modeling, and responding to student…

On the Application of Syntactic Methodologies in Automatic Text Analysis.

ERIC Educational Resources Information Center

Salton, Gerard; And Others

1990-01-01

Summarizes various linguistic approaches proposed for document analysis in information retrieval environments. Topics discussed include syntactic analysis; use of machine-readable dictionary information; knowledge base construction; the PLNLP English Grammar (PEG) system; phrase normalization; and statistical and syntactic phrase evaluation used…

Comprehension Challenges in the Fourth Grade: The Roles of Text Cohesion, Text Genre, and Readers' Prior Knowledge

ERIC Educational Resources Information Center

McNamara, Danielle S.; Ozuru, Yasuhiro; Floyd, Randy G.

2011-01-01

We examined young readers' comprehension as a function of text genre (narrative, science), text cohesion (high, low), and readers' abilities (reading decoding skills and world knowledge). The overarching purpose of this study was to contribute to our understanding of the "fourth grade slump". Children in grade 4 read four texts,…

Navigation and Comprehension of Digital Expository Texts: Hypertext Structure, Previous Domain Knowledge, and Working Memory Capacity

ERIC Educational Resources Information Center

Burin, Debora I.; Barreyro, Juan P.; Saux, Gastón; Irrazábal, Natalia C.

2015-01-01

Introduction: In contemporary information societies, reading digital text has become pervasive. One of the most distinctive features of digital texts is their internal connections via hyperlinks, resulting in non-linear hypertexts. Hypertext structure and previous knowledge affect navigation and comprehension of digital expository texts. From the…

A Knowledge Discovery framework for Planetary Defense

NASA Astrophysics Data System (ADS)

Jiang, Y.; Yang, C. P.; Li, Y.; Yu, M.; Bambacus, M.; Seery, B.; Barbee, B.

2016-12-01

Planetary Defense, a project funded by NASA Goddard and the NSF, is a multi-faceted effort focused on the mitigation of Near Earth Object (NEO) threats to our planet. Currently, there exists a dispersion of information concerning NEO's amongst different organizations and scientists, leading to a lack of a coherent system of information to be used for efficient NEO mitigation. In this paper, a planetary defense knowledge discovery engine is proposed to better assist the development and integration of a NEO responding system. Specifically, we have implemented an organized information framework by two means: 1) the development of a semantic knowledge base, which provides a structure for relevant information. It has been developed by the implementation of web crawling and natural language processing techniques, which allows us to collect and store the most relevant structured information on a regular basis. 2) the development of a knowledge discovery engine, which allows for the efficient retrieval of information from our knowledge base. The knowledge discovery engine has been built on the top of Elasticsearch, an open source full-text search engine, as well as cutting-edge machine learning ranking and recommendation algorithms. This proposed framework is expected to advance the knowledge discovery and innovation in planetary science domain.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2001-01-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2000-12-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

WebMail versus WebApp: Comparing Problem-Based Learning Methods in a Business Research Methods Course

ERIC Educational Resources Information Center

Williams van Rooij, Shahron

2007-01-01

This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…

Designing Online Learning. Knowledge Series: A Topical, Start-Up Guide to Distance Education Practice and Delivery.

ERIC Educational Resources Information Center

Mishra, Sanjaya

The term "online learning" refers to an Internet- or intranet-based teaching and learning system designed for World Wide Web-based delivery without face-to-face contact between teacher and learner. The Internet is the backbone of online learning. The following media are available to designers of online courses: text; graphics and images;…

Statistics for Learning Genetics

ERIC Educational Resources Information Center

Charles, Abigail Sheena

2012-01-01

This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing…

Learning Stoichiometry: A Comparison of Text and Multimedia Formats

ERIC Educational Resources Information Center

Evans, Karen L.; Yaron, David; Leinhardt, Gaea

2008-01-01

Even after repeated instruction, first year college chemistry students are often unable to apply stoichiometry knowledge to equilibrium and acid-base chemistry problems. The dynamic and interactive capabilities of online technology may facilitate stoichiometry instruction that promotes more meaningful learning. This study compares a…

Facilitating Decision Making, Re-Use and Collaboration: A Knowledge Management Approach to Acquisition Program Self-Awareness

DTIC Science & Technology

2009-06-01

capabilities: web-based, relational/multi-dimensional, client/server, and metadata (data about data) inclusion (pp. 39-40). Text mining, on the other...and Organizational Systems ( CASOS ) (Carley, 2005). Although AutoMap can be used to conduct text-mining, it was utilized only for its visualization...provides insight into how the GMCOI is using the terms, and where there might be redundant terms and need for de -confliction and standardization

Extracting BI-RADS Features from Portuguese Clinical Texts

PubMed Central

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C.; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2013-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser’s performance is comparable to the manual method. PMID:23797461

Semantic annotation in biomedicine: the current landscape.

PubMed

Jovanović, Jelena; Bagheri, Ebrahim

2017-09-22

The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.

Comparing comprehension measured by multiple-choice and open-ended questions.

PubMed

Ozuru, Yasuhiro; Briner, Stephen; Kurby, Christopher A; McNamara, Danielle S

2013-09-01

This study compared the nature of text comprehension as measured by multiple-choice format and open-ended format questions. Participants read a short text while explaining preselected sentences. After reading the text, participants answered open-ended and multiple-choice versions of the same questions based on their memory of the text content. The results indicated that performance on open-ended questions was correlated with the quality of self-explanations, but performance on multiple-choice questions was correlated with the level of prior knowledge related to the text. These results suggest that open-ended and multiple-choice format questions measure different aspects of comprehension processes. The results are discussed in terms of dual process theories of text comprehension. PsycINFO Database Record (c) 2013 APA, all rights reserved

Teaching science through literature

NASA Astrophysics Data System (ADS)

Barth, Daniel

2007-12-01

The hypothesis of this study was that a multidisciplinary, activity rich science curriculum based around science fiction literature, rather than a conventional text book would increase student engagement with the curriculum and improve student performance on standards-based test instruments. Science fiction literature was chosen upon the basis of previous educational research which indicated that science fiction literature was able to stimulate and maintain interest in science. The study was conducted on a middle school campus during the regular summer school session. Students were self-selected from the school's 6 th, 7th, and 8th grade populations. The students used the science fiction novel Maurice on the Moon as their only text. Lessons and activities closely followed the adventures of the characters in the book. The student's initial level of knowledge in Earth and space science was assessed by a pre test. After the four week program was concluded, the students took a post test made up of an identical set of questions. The test included 40 standards-based questions that were based upon concepts covered in the text of the novel and in the classroom lessons and activities. The test also included 10 general knowledge questions that were based upon Earth and space science standards that were not covered in the novel or the classroom lessons or activities. Student performance on the standards-based question set increased an average of 35% for all students in the study group. Every subgroup disaggregated by gender and ethnicity improved from 28-47%. There was no statistically significant change in the performance on the general knowledge question set for any subgroup. Student engagement with the material was assessed by three independent methods, including student self-reports, percentage of classroom work completed, and academic evaluation of student work by the instructor. These assessments of student engagement were correlated with changes in student performance on the standards-based assessment tests. A moderate correlation was found to exist between the level of student engagement with the material and improvement in performance from pre to post test.

Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature

PubMed Central

Xu, Rong; Li, Li; Wang, QuanQiu

2013-01-01

Motivation: Systems approaches to studying phenotypic relationships among diseases are emerging as an active area of research for both novel disease gene discovery and drug repurposing. Currently, systematic study of disease phenotypic relationships on a phenome-wide scale is limited because large-scale machine-understandable disease–phenotype relationship knowledge bases are often unavailable. Here, we present an automatic approach to extract disease–manifestation (D-M) pairs (one specific type of disease–phenotype relationship) from the wide body of published biomedical literature. Data and Methods: Our method leverages external knowledge and limits the amount of human effort required. For the text corpus, we used 119 085 682 MEDLINE sentences (21 354 075 citations). First, we used D-M pairs from existing biomedical ontologies as prior knowledge to automatically discover D-M–specific syntactic patterns. We then extracted additional pairs from MEDLINE using the learned patterns. Finally, we analysed correlations between disease manifestations and disease-associated genes and drugs to demonstrate the potential of this newly created knowledge base in disease gene discovery and drug repurposing. Results: In total, we extracted 121 359 unique D-M pairs with a high precision of 0.924. Among the extracted pairs, 120 419 (99.2%) have not been captured in existing structured knowledge sources. We have shown that disease manifestations correlate positively with both disease-associated genes and drug treatments. Conclusions: The main contribution of our study is the creation of a large-scale and accurate D-M phenotype relationship knowledge base. This unique knowledge base, when combined with existing phenotypic, genetic and proteomic datasets, can have profound implications in our deeper understanding of disease etiology and in rapid drug repurposing. Availability: http://nlp.case.edu/public/data/DMPatternUMLS/ Contact: rxx@case.edu PMID:23828786

Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge

PubMed Central

2012-01-01

Objectives This study demonstrates the feasibility of using expert system shells for rapid clinical decision support module development. Methods A readily available expert system shell was used to build a simple rule-based system for the crude diagnosis of vaginal discharge. Pictures and 'canned text explanations' are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the system are documented. Results The system runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program's executable and the commented knowledge base source as a text document). The limitations of the demonstration system, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the system, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. Conclusions An easy-to-use expert system shell enables clinicians to rapidly become their own 'knowledge engineers' and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a service to the clinical community. PMID:23346475

BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Pafilis, Evangelos; Theodosiou, Theodosios; Schneider, Reinhard; Satagopam, Venkata P; Ouzounis, Christos A; Eliopoulos, Aristides G; Promponas, Vasilis J; Iliopoulos, Ioannis

2014-11-15

The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A randomized study of multimedia informational aids for research on medical practices: implications for informed consent

PubMed Central

Kraft, Stephanie A; Constantine, Melissa; Magnus, David; Porter, Kathryn M.; Lee, Sandra Soo-Jin; Green, Michael; Kass, Nancy E; Wilfond, Benjamin S.; Cho, Mildred K

2016-01-01

Background/aims Participant understanding is a key element of informed consent for enrollment in research. However, participants often do not understand the nature, risks, benefits, or design of the studies in which they take part. Research on medical practices, which studies standard interventions rather than new treatments, has the potential to be especially confusing to participants because it is embedded within usual clinical care. Our objective in this randomized study was to compare the ability of a range of multimedia informational aids to improve participant understanding in the context of research on medical practices. Methods We administered a Web-based survey to members of a proprietary online panel sample selected to match national U.S. demographics. Respondents were randomized to one of five arms: four content-equivalent informational aids (animated videos, slideshows with voiceover, comics, and text), and one no-intervention control. We measured knowledge of research on medical practices using a summary knowledge score from 10 questions based on the content of the informational aids. We used ANOVA and paired t-tests to compare knowledge scores between arms. Results There were 1500 completed surveys (300 in each arm). Mean knowledge scores were highest for the slideshows with voiceover (65.7%), followed by the animated videos (62.7%), comics (60.7%), text (57.2%), and control (50.3%). Differences between arms were statistically significant except between the slideshows with voiceover and animated videos and between the animated videos and comics. Informational aids that included an audio component (animated videos and slideshows with voiceover) had higher knowledge scores than those without an audio component (64.2% versus 59.0%, p<.0001). There was no difference between informational aids with a character-driven story component (animated videos and comics) and those without. Conclusions Our results show that simple multimedia aids that use a dual-channel approach, such as voiceover with visual reinforcement, can improve participant knowledge more effectively than text alone. However, the relatively low knowledge scores suggest that targeted informational aids may be needed to teach some particularly challenging concepts. Nonetheless, our results demonstrate the potential to improve informed consent for research on medical practices by using multimedia aids that include simplified language and visual metaphors. PMID:27625314

A randomized study of multimedia informational aids for research on medical practices: Implications for informed consent.

PubMed

Kraft, Stephanie A; Constantine, Melissa; Magnus, David; Porter, Kathryn M; Lee, Sandra Soo-Jin; Green, Michael; Kass, Nancy E; Wilfond, Benjamin S; Cho, Mildred K

2017-02-01

Participant understanding is a key element of informed consent for enrollment in research. However, participants often do not understand the nature, risks, benefits, or design of the studies in which they take part. Research on medical practices, which studies standard interventions rather than new treatments, has the potential to be especially confusing to participants because it is embedded within usual clinical care. Our objective in this randomized study was to compare the ability of a range of multimedia informational aids to improve participant understanding in the context of research on medical practices. We administered a web-based survey to members of a proprietary online panel sample selected to match national US demographics. Respondents were randomized to one of five arms: four content-equivalent informational aids (animated videos, slideshows with voice-over, comics, and text) and one no-intervention control. We measured knowledge of research on medical practices using a summary knowledge score from 10 questions based on the content of the informational aids. We used analysis of variance and paired t-tests to compare knowledge scores between arms. There were 1500 completed surveys (300 in each arm). Mean knowledge scores were highest for the slideshows with voice-over (65.7%), followed by the animated videos (62.7%), comics (60.7%), text (57.2%), and control (50.3%). Differences between arms were statistically significant except between the slideshows with voice-over and animated videos and between the animated videos and comics. Informational aids that included an audio component (animated videos and slideshows with voice-over) had higher knowledge scores than those without an audio component (64.2% vs 59.0%, p < .0001). There was no difference between informational aids with a character-driven story component (animated videos and comics) and those without. Our results show that simple multimedia aids that use a dual-channel approach, such as voice-over with visual reinforcement, can improve participant knowledge more effectively than text alone. However, the relatively low knowledge scores suggest that targeted informational aids may be needed to teach some particularly challenging concepts. Nonetheless, our results demonstrate the potential to improve informed consent for research on medical practices using multimedia aids that include simplified language and visual metaphors.

Knowledge Discovery in Literature Data Bases

NASA Astrophysics Data System (ADS)

Albrecht, Rudolf; Merkl, Dieter

The concept of knowledge discovery as defined through ``establishing previously unknown and unsuspected relations of features in a data base'' is, cum grano salis, relatively easy to implement for data bases containing numerical data. Increasingly we find at our disposal data bases containing scientific literature. Computer assisted detection of unknown relations of features in such data bases would be extremely valuable and would lead to new scientific insights. However, the current representation of scientific knowledge in such data bases is not conducive to computer processing. Any correlation of features still has to be done by the human reader, a process which is plagued by ineffectiveness and incompleteness. On the other hand we note that considerable progress is being made in an area where reading all available material is totally prohibitive: the World Wide Web. Robots and Web crawlers mine the Web continuously and construct data bases which allow the identification of pages of interest in near real time. An obvious step is to categorize and classify the documents in the text data base. This can be used to identify papers worth reading, or which are of unexpected cross-relevance. We show the results of first experiments using unsupervised classification based on neural networks.

How can everyday practical knowledge be understood with inspiration from philosophy?

PubMed

Lykkeslet, Else; Gjengedal, Eva

2006-04-01

Many nursing scholars are inspired by philosophy when investigating phenomena within nursing. This paper focuses on the everyday practical knowledge of nurses. Based on an empirical project carried out in a surgical ward the authors make an attempt, with help from philosophy, at identifying and conceptualizing elements of knowledge in everyday practice. With reference to texts by Heidegger and Wittgenstein the authors investigate two dimensions of nursing knowledge: a dimension of doing and a dimension of being. These dimensions are further developed and concretized in the paper. The doing dimension is emphasized through the concepts of adapting and exploring. The being dimension has its basis in being understanding and being connected. These two dimensions constitute a form of knowledge which is mobile and flexible. This knowledge is in place in everyday situations and it works where it is supposed to work.

PuReD-MCL: a graph-based PubMed document clustering methodology.

PubMed

Theodosiou, T; Darzentas, N; Angelis, L; Ouzounis, C A

2008-09-01

Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed. PuReD-MCL avoids using natural language processing (NLP) techniques directly; instead, it takes advantage of existing resources, available from PubMed. PuReD-MCL then clusters documents efficiently using the MCL graph clustering algorithm, which is based on graph flow simulation. This process allows users to analyse the results by highlighting important clues, and finally to visualize the clusters and all relevant information using an interactive graph layout algorithm, for instance BioLayout Express 3D. The methodology was applied to two different datasets, previously used for the validation of the document clustering tool TextQuest. The first dataset involves the organisms Escherichia coli and yeast, whereas the second is related to Drosophila development. PuReD-MCL successfully reproduces the annotated results obtained from TextQuest, while at the same time provides additional insights into the clusters and the corresponding documents. Source code in perl and R are available from http://tartara.csd.auth.gr/~theodos/

Prior Conceptual Knowledge and Textbook Search.

ERIC Educational Resources Information Center

Byrnes, James P.; Guthrie, John T.

1992-01-01

The role of a subject's conceptual knowledge in the procedural task of searching a text for information was studied for 51 college undergraduates in 2 experiments involving knowledge of anatomy. Students with more anatomical information were able to search a text more quickly. Educational implications are discussed. (SLD)

How Knowledge Powers Reading

ERIC Educational Resources Information Center

Lemov, Doug

2017-01-01

Recent research shows that reading comprehension relies heavily on prior knowledge. Far more than generic "reading skills" like drawing inferences, making predictions, and knowing the function of subheads, how well students learn from a nonfiction text depends on their background knowledge of the text's subject matter. And in a cyclical…

Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

PubMed

Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

2017-01-01

Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

Individual Differences in Reprocessing of Text.

ERIC Educational Resources Information Center

Haenggi, Dieter; Perfetti, Charles A.

1992-01-01

Decoding, working memory, and domain-specific prior knowledge were studied as predictors of comprehension for 48 university undergraduate students after rewriting notes, rereading notes, or rereading a text. Working memory was most important for comprehension of text-implicit information, whereas knowledge was relatively more important for…

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

Incorporating Semantics into Data Driven Workflows for Content Based Analysis

NASA Astrophysics Data System (ADS)

Argüello, M.; Fernandez-Prieto, M. J.

Finding meaningful associations between text elements and knowledge structures within clinical narratives in a highly verbal domain, such as psychiatry, is a challenging goal. The research presented here uses a small corpus of case histories and brings into play pre-existing knowledge, and therefore, complements other approaches that use large corpus (millions of words) and no pre-existing knowledge. The paper describes a variety of experiments for content-based analysis: Linguistic Analysis using NLP-oriented approaches, Sentiment Analysis, and Semantically Meaningful Analysis. Although it is not standard practice, the paper advocates providing automatic support to annotate the functionality as well as the data for each experiment by performing semantic annotation that uses OWL and OWL-S. Lessons learnt can be transmitted to legacy clinical databases facing the conversion of clinical narratives according to prominent Electronic Health Records standards.

A Semantic Lexicon-Based Approach for Sense Disambiguation and Its WWW Application

NASA Astrophysics Data System (ADS)

di Lecce, Vincenzo; Calabrese, Marco; Soldo, Domenico

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts.

Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014

DTIC Science & Technology

2014-11-01

formation about the entity, every new document would drive an update to the entity profile, strongly suggesting vitalness. Figure 3 represents...of the Twenty-Second Text REtrieval Conference (TREC 2013), 2013. Caruana, Richard. Multitask Learning: A Knowledge-Based Source of Inductive Bias. In

Improving learning with science and social studies text using computer-based concept maps for students with disabilities.

PubMed

Ciullo, Stephen; Falcomata, Terry S; Pfannenstiel, Kathleen; Billingsley, Glenna

2015-01-01

Concept maps have been used to help students with learning disabilities (LD) improve literacy skills and content learning, predominantly in secondary school. However, despite increased access to classroom technology, no previous studies have examined the efficacy of computer-based concept maps to improve learning from informational text for students with LD in elementary school. In this study, we used a concurrent delayed multiple probe design to evaluate the interactive use of computer-based concept maps on content acquisition with science and social studies texts for Hispanic students with LD in Grades 4 and 5. Findings from this study suggest that students improved content knowledge during intervention relative to a traditional instruction baseline condition. Learning outcomes and social validity information are considered to inform recommendations for future research and the feasibility of classroom implementation. © The Author(s) 2014.

DrugQuest - a text mining workflow for drug association discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Benefits and Limitations of Text Messages to Stimulate Higher Learning Among Community Providers: Participants' Views of an mHealth Intervention to Support Continuing Medical Education in Vietnam

PubMed Central

Sabin, Lora L; Larson Williams, Anna; Le, Bao Ngoc; Herman, Augusta R; Viet Nguyen, Ha; Albanese, Rebecca R; Xiong, Wenjun; Shobiye, Hezekiah OA; Halim, Nafisa; Tran, Lien Thi Ngoc; McNabb, Marion; Hoang, Hai; Falconer, Ariel; Nguyen, Tam Thi Thanh; Gill, Christopher J

2017-01-01

Background: A randomized controlled trial was conducted in 2015 to evaluate a mobile continuing medical education (mCME) intervention that provided daily text messages to community-based physicians' assistants (CBPAs) in Thai Nguyen Province, Vietnam. Although the intervention failed to improve medical knowledge over a 6-month period, a companion qualitative study provided insights on the views and experiences of intervention participants. Methods: We conducted focus group discussions (FGDs) and in-depth interviews (IDIs) among participants randomized to receive text messages containing either simple medical facts or quiz questions. Trained interviewers collected data immediately following the conclusion of the trial in December 2015. Using semi-structured question guides, respondents were queried on their views of the intervention, positive and negative, and perceived impacts of the intervention. During analysis, after learning that the intervention had failed to increase knowledge among participants, we also examined reasons for lack of improvement in medical knowledge. All analyses were performed in NVivo using a thematic approach. Results: A total of 70 CBPAs engaged in one of 8 FGDs or an IDI. One-half were men; average age among all respondents was 40 years. Most (81%) practiced in rural settings and most (51%) focused on general medicine. The mean length of work experience was 3 years. All respondents made positive comments about the intervention; convenience, relevance, and quick feedback (quiz format) were praised. Downsides encompassed lack of depth of information, weak interaction, technology challenges, and challenging/irrelevant messages. Respondents described perceived impacts encompassing increased motivation, knowledge, collegial discussions, Internet use to search for more information, and clinical skills. Overall, they expressed a desire for the intervention to continue and recommended expansion to other medical professionals. Overreliance on the text messages, lack of effective self-study, and technical/language-based barriers may be potential explanations for intervention failure. Conclusion: As a form of mCME, daily text messages were well-received by community-level health care providers in Vietnam. This mCME approach appears very promising in low-resource environments or where traditional forms of CME are impractical. Future models might consider enhancements to foster linkages to relevant medical materials, improve interaction with medical experts, and tailor medical content to the daily activities of medical staff. PMID:28655802

Benefits and Limitations of Text Messages to Stimulate Higher Learning Among Community Providers: Participants' Views of an mHealth Intervention to Support Continuing Medical Education in Vietnam.

PubMed

Sabin, Lora L; Larson Williams, Anna; Le, Bao Ngoc; Herman, Augusta R; Viet Nguyen, Ha; Albanese, Rebecca R; Xiong, Wenjun; Shobiye, Hezekiah Oa; Halim, Nafisa; Tran, Lien Thi Ngoc; McNabb, Marion; Hoang, Hai; Falconer, Ariel; Nguyen, Tam Thi Thanh; Gill, Christopher J

2017-06-27

A randomized controlled trial was conducted in 2015 to evaluate a mobile continuing medical education (mCME) intervention that provided daily text messages to community-based physicians' assistants (CBPAs) in Thai Nguyen Province, Vietnam. Although the intervention failed to improve medical knowledge over a 6-month period, a companion qualitative study provided insights on the views and experiences of intervention participants. We conducted focus group discussions (FGDs) and in-depth interviews (IDIs) among participants randomized to receive text messages containing either simple medical facts or quiz questions. Trained interviewers collected data immediately following the conclusion of the trial in December 2015. Using semi-structured question guides, respondents were queried on their views of the intervention, positive and negative, and perceived impacts of the intervention. During analysis, after learning that the intervention had failed to increase knowledge among participants, we also examined reasons for lack of improvement in medical knowledge. All analyses were performed in NVivo using a thematic approach. A total of 70 CBPAs engaged in one of 8 FGDs or an IDI. One-half were men; average age among all respondents was 40 years. Most (81%) practiced in rural settings and most (51%) focused on general medicine. The mean length of work experience was 3 years. All respondents made positive comments about the intervention; convenience, relevance, and quick feedback (quiz format) were praised. Downsides encompassed lack of depth of information, weak interaction, technology challenges, and challenging/irrelevant messages. Respondents described perceived impacts encompassing increased motivation, knowledge, collegial discussions, Internet use to search for more information, and clinical skills. Overall, they expressed a desire for the intervention to continue and recommended expansion to other medical professionals. Overreliance on the text messages, lack of effective self-study, and technical/language-based barriers may be potential explanations for intervention failure. As a form of mCME, daily text messages were well-received by community-level health care providers in Vietnam. This mCME approach appears very promising in low-resource environments or where traditional forms of CME are impractical. Future models might consider enhancements to foster linkages to relevant medical materials, improve interaction with medical experts, and tailor medical content to the daily activities of medical staff. © Sabin et al.

Signaling Text-Picture Relations in Multimedia Learning: The Influence of Prior Knowledge

ERIC Educational Resources Information Center

Richter, Juliane; Scheiter, Katharina; Eitel, Alexander

2018-01-01

Multimedia integration signals highlight correspondences between text and pictures with the aim of supporting learning from multimedia. A recent meta-analysis revealed that only learners with low domain-specific prior knowledge benefit from multimedia integration signals. To more thoroughly investigate the influence of prior knowledge on the…

The effects of activating prior topic and metacognitive knowledge on text comprehension scores.

PubMed

Kostons, Danny; van der Werf, Greetje

2015-09-01

Research on prior knowledge activation has consistently shown that activating learners' prior knowledge has beneficial effects on learning. If learners activate their prior knowledge, this activated knowledge serves as a framework for establishing relationships between the knowledge they already possess and new information provided to them. Thus far, prior knowledge activation has dealt primarily with topic knowledge in specific domains. Students, however, likely also possess at least some metacognitive knowledge useful in those domains, which, when activated, should aid in the deployment of helpful strategies during reading. In this study, we investigated the effects of both prior topic knowledge activation (PTKA) and prior metacognitive knowledge activation (PMKA) on text comprehension scores. Eighty-eight students in primary education were randomly distributed amongst the conditions of the 2 × 2 (PTKA yes/no × PMKA yes/no) designed experiment. Results show that activating prior metacognitive knowledge had a beneficial effect on text comprehension, whereas activating prior topic knowledge, after correcting for the amount of prior knowledge, did not. Most studies deal with explicit instruction of metacognitive knowledge, but our results show that this may not be necessary, specifically in the case of students who already have some metacognitive knowledge. However, existing metacognitive knowledge needs to be activated in order for students to make better use of this knowledge. © 2015 The British Psychological Society.

Chapter 16: text mining for translational bioinformatics.

PubMed

Cohen, K Bretonnel; Hunter, Lawrence E

2013-04-01

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Jointly learning word embeddings using a corpus and a knowledge base

PubMed Central

Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi

2018-01-01

Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words. We combine the knowledge in both a corpus and a KB to learn better word embeddings. Specifically, we propose a joint word representation learning method that uses the knowledge in the KBs, and simultaneously predicts the co-occurrences of two words in a corpus context. In particular, we use the corpus to define our objective function subject to the relational constrains derived from the KB. We further utilise the corpus co-occurrence statistics to propose two novel approaches, Nearest Neighbour Expansion (NNE) and Hedged Nearest Neighbour Expansion (HNE), that dynamically expand the KB and therefore derive more constraints that guide the optimisation process. Our experimental results over a wide-range of benchmark tasks demonstrate that the proposed method statistically significantly improves the accuracy of the word embeddings learnt. It outperforms a corpus-only baseline and reports an improvement of a number of previously proposed methods that incorporate corpora and KBs in both semantic similarity prediction and word analogy detection tasks. PMID:29529052

Learning stoichiometry: A comparison of text and multimedia instructional formats

NASA Astrophysics Data System (ADS)

Evans, Karen L.

Even after multiple instructional opportunities, first year college chemistry students are often unable to apply stoichiometry knowledge in equilibrium and acid-base chemistry problem solving. Cognitive research findings suggest that for learning to be meaningful, learners need to actively construct their own knowledge by integrating new information into, and reorganizing, their prior understandings. Scaffolded inquiry in which facts, procedures, and principles are introduced as needed within the context of authentic problem solving may provide the practice and encoding opportunities necessary for construction of a memorable and usable knowledge base. The dynamic and interactive capabilities of online technology may facilitate stoichiometry instruction that promotes this meaningful learning. Entering college freshmen were randomly assigned to either a technology-rich or text-only set of cognitively informed stoichiometry review materials. Analysis of posttest scores revealed a significant but small difference in the performance of the two treatment groups, with the technology-rich group having the advantage. Both SAT and gender, however, explained more of the variability in the scores. Analysis of the posttest scores from the technology-rich treatment group revealed that the degree of interaction with the Virtual Lab simulation was significantly related to posttest performance and subsumed any effect of prior knowledge as measured by SAT scores. Future users of the online course should be encouraged to engage with the problem-solving opportunities provided by the Virtual Lab simulation through either explicit instruction and/or implementation of some level of program control within the course's navigational features.

Presentation planning using an integrated knowledge base

NASA Technical Reports Server (NTRS)

Arens, Yigal; Miller, Lawrence; Sondheimer, Norman

1988-01-01

A description is given of user interface research aimed at bringing together multiple input and output modes in a way that handles mixed mode input (commands, menus, forms, natural language), interacts with a diverse collection of underlying software utilities in a uniform way, and presents the results through a combination of output modes including natural language text, maps, charts and graphs. The system, Integrated Interfaces, derives much of its ability to interact uniformly with the user and the underlying services and to build its presentations, from the information present in a central knowledge base. This knowledge base integrates models of the application domain (Navy ships in the Pacific region, in the current demonstration version); the structure of visual displays and their graphical features; the underlying services (data bases and expert systems); and interface functions. The emphasis is on a presentation planner that uses the knowledge base to produce multi-modal output. There has been a flurry of recent work in user interface management systems. (Several recent examples are listed in the references). Existing work is characterized by an attempt to relieve the software designer of the burden of handcrafting an interface for each application. The work has generally focused on intelligently handling input. This paper deals with the other end of the pipeline - presentations.

Text Prep

ERIC Educational Resources Information Center

Buehl, Doug

2017-01-01

To understand complex disciplinary texts, students need to possess a rich store of background knowledge. But what happens if students don't have that knowledge? In this article, Doug Buehl explores frontloading strategies that can bridge the gap between what students know and what they need to know to comprehend a disciplinary text. He outlines…

Unsupervised Ontology Generation from Unstructured Text. CRESST Report 827

ERIC Educational Resources Information Center

Mousavi, Hamid; Kerr, Deirdre; Iseli, Markus R.

2013-01-01

Ontologies are a vital component of most knowledge acquisition systems, and recently there has been a huge demand for generating ontologies automatically since manual or supervised techniques are not scalable. In this paper, we introduce "OntoMiner", a rule-based, iterative method to extract and populate ontologies from unstructured or…

Mentoring and supervision in healthcare Neil Gopee Mentoring and supervision in healthcare Sage 224 £19.99 978-1412930208 1412930200 [Formula: see text].

PubMed

2008-09-13

AUTHOR NEIL GOPEE explores the standards, competencies and outcomes for mentoring in health care. He reviews the current knowledge, skills and attitudes required of mentors, and successfully links these with research-based literature.

Video Games to Reading: Reaching out to Reluctant Readers

ERIC Educational Resources Information Center

Jolley, Kristie

2008-01-01

Junior high school teacher Kristie Jolley believes students become more willing and motivated to practice reading strategies when they are "comfortable within their realm of literacy." Background knowledge of video games helps students succeed in understanding and enjoying game-based texts, which she incorporates into her classroom library as…

Common Knowledge, Learning, and Citation Practices in University Writing

ERIC Educational Resources Information Center

Shi, Ling

2011-01-01

The present study is based on interviews of students (n = 48) and instructors (n = 27) from various disciplines in a North American research university and explores participants' comments on examples of some students' unacknowledged texts appropriated and drawn from published sources, classroom learning, or unidentified prior reading. Although…

Linguistically Motivated Features for CCG Realization Ranking

ERIC Educational Resources Information Center

Rajkumar, Rajakrishnan

2012-01-01

Natural Language Generation (NLG) is the process of generating natural language text from an input, which is a communicative goal and a database or knowledge base. Informally, the architecture of a standard NLG system consists of the following modules (Reiter and Dale, 2000): content determination, sentence planning (or microplanning) and surface…

Evaluating the Efficacy of Remediation for Struggling Readers in High School

ERIC Educational Resources Information Center

Lovett, Maureen W.; Lacerenza, Lea; De Palma, Maria; Frijters, Jan C.

2012-01-01

Preliminary efficacy data are reported for a research-based reading intervention designed for struggling readers in high school. PHAST PACES teaches (a) word identification strategies, (b) knowledge of text structures, and (c) reading comprehension strategies. In a quasi-experimental design, 268 intervention and 83 waiting list control students…

Multifaceted Assessment for Early Childhood Education

ERIC Educational Resources Information Center

Wright, Robert J.

2010-01-01

The book is a highly readable integration of the latest assessment policies, and includes valuable information regarding young children with special needs and English Language Learners--topics that have rarely been touched upon in other textbooks. Focusing on practical applications of key concepts, this text provides a knowledge base of what every…

Schema-Based Text Comprehension

ERIC Educational Resources Information Center

Ensar, Ferhat

2015-01-01

Schema is one of the most common terms used for classifying and constructing knowledge. Therefore, a schema is a pre-planned set of concepts. It usually contains social information and is used to represent chain of events, perceptions, situations, relationships and even objects. For example, Kant initially defines the idea of schema as some…

Multicultural Counseling in Schools: A Synergetic Approach.

ERIC Educational Resources Information Center

Herring, Roger D.

As the percentage of ethnic minority students in schools continues to increase, school counselors and counselors-in-training must broaden their cultural knowledge base and develop new strategies that are responsive to the complex challenges these students face. This text provides direction for working within the ethnic minority student's worldview…

Does prior domain-specific content knowledge influence students' recall of arguments surrounding interdisciplinary topics?

PubMed

Schmidt, Hiemke K; Rothgangel, Martin; Grube, Dietmar

2017-12-01

Awareness of various arguments can help interactants present opinions, stress points, and build counterarguments during discussions. At school, some topics are taught in a way that students learn to accumulate knowledge and gather arguments, and later employ them during debates. Prior knowledge may facilitate recalling information on well structured, fact-based topics, but does it facilitate recalling arguments during discussions on complex, interdisciplinary topics? We assessed the prior knowledge in domains related to a bioethical topic of 277 students from Germany (approximately 15 years old), their interest in the topic, and their general knowledge. The students read a text with arguments for and against prenatal diagnostics and tried to recall the arguments one week later and again six weeks later. Prior knowledge in various domains related to the topic individually and separately helped students recall the arguments. These relationships were independent of students' interest in the topic and their general knowledge. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

How do physicians become medical experts? A test of three competing theories: distinct domains, independent influence and encapsulation models.

PubMed

Violato, Claudio; Gao, Hong; O'Brien, Mary Claire; Grier, David; Shen, E

2018-05-01

The distinction between basic sciences and clinical knowledge which has led to a theoretical debate on how medical expertise is developed has implications for medical school and lifelong medical education. This longitudinal, population based observational study was conducted to test the fit of three theories-knowledge encapsulation, independent influence, distinct domains-of the development of medical expertise employing structural equation modelling. Data were collected from 548 physicians (292 men-53.3%; 256 women-46.7%; mean age = 24.2 years on admission) who had graduated from medical school 2009-2014. They included (1) Admissions data of undergraduate grade point average and Medical College Admission Test sub-test scores, (2) Course performance data from years 1, 2, and 3 of medical school, and (3) Performance on the NBME exams (i.e., Step 1, Step 2 CK, and Step 3). Statistical fit indices (Goodness of Fit Index-GFI; standardized root mean squared residual-SRMR; root mean squared error of approximation-RSMEA) and comparative fit [Formula: see text] of three theories of cognitive development of medical expertise were used to assess model fit. There is support for the knowledge encapsulation three factor model of clinical competency (GFI = 0.973, SRMR = 0.043, RSMEA = 0.063) which had superior fit indices to both the independent influence and distinct domains theories ([Formula: see text] vs [Formula: see text] [[Formula: see text

The Role of Prior Knowledge in Learning from Analogies in Science Texts

ERIC Educational Resources Information Center

Braasch, Jason L. G.; Goldman, Susan R.

2010-01-01

Two experiments examined whether inconsistent effects of analogies in promoting new content learning from text are related to prior knowledge of the analogy "per se." In Experiment 1, college students who demonstrated little understanding of weather systems and different levels of prior knowledge (more vs. less) of an analogous everyday…

On the Relationship between Morphology Knowledge and Quality of Translation

ERIC Educational Resources Information Center

Arbabi Aski, Mohammadreza

2008-01-01

The present study intended to investigate whether there is any relationship between morphological knowledge and quality of legal text translation from English to Persian and to what extent do Iranian M.A students of translation use morphological knowledge to guess the meaning of words when translating legal texts from English to Persian. To…

A common type system for clinical natural language processing

PubMed Central

2013-01-01

Background One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. Conclusions We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types. PMID:23286462

A common type system for clinical natural language processing.

PubMed

Wu, Stephen T; Kaggal, Vinod C; Dligach, Dmitriy; Masanz, James J; Chen, Pei; Becker, Lee; Chapman, Wendy W; Savova, Guergana K; Liu, Hongfang; Chute, Christopher G

2013-01-03

One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts.

PubMed

Sharma, Vivekanand; Law, Wayne; Balick, Michael J; Sarkar, Indra Neil

2017-01-01

The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement.

Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts

PubMed Central

Sharma, Vivekanand; Law, Wayne; Balick, Michael J.; Sarkar, Indra Neil

2017-01-01

The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement. PMID:29854223

A Pilot Randomized Trial of Text-Messaging for Symptom Awareness and Diabetes Knowledge in Adolescents With Type 1 Diabetes

PubMed Central

Han, Yi; Faulkner, Melissa Spezia; Fritz, Heather; Fadoju, Doris; Muir, Andrew; Abowd, Gregory D.; Head, Lauren; Arriaga, Rosa I.

2015-01-01

Adolescents with type 1 diabetes typically receive clinical care every 3 months. Between visits, diabetes-related issues may not be frequently reflected, learned, and documented by the patients, limiting their self-awareness and knowledge about their condition. We designed a text-messaging system to help resolve this problem. In a pilot, randomized controlled trial with 30 adolescents, we examined the effect of text messages about symptom awareness and diabetes knowledge on glucose control and quality of life. The intervention group that received more text messages between visits had significant improvements in quality of life. PMID:25720675

Helping Children Become More Knowledgeable through Text

ERIC Educational Resources Information Center

Neuman, Susan B.; Roskos, Kathleen

2012-01-01

With the adoption of the Common Core State Standards, curriculum resources are shifting from an emphasis on literary texts to a greater focus on informational texts. Although we need to understand the intention of these new Common Core State Standards, and the important drive toward greater content knowledge for all students, we must be wary of…

Pattern-Directed Processing of Knowledge from Texts.

ERIC Educational Resources Information Center

Thorndyke, Perry W.

A framework for viewing human text comprehension, memory, and recall is presented that assumes patterns of abstract conceptual relations are used to guide processing. These patterns consist of clusters of knowledge that encode prototypical co-occurrences of situations and events in narrative texts. The patterns are assumed to be a part of a…

Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

PubMed

Olsher, Daniel

2014-10-01

Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

[The treatment of scientific knowledge in the framework of CITES].

PubMed

Lanfranchi, Marie-Pierre

2014-03-01

Access to scientific knowledge in the context of CITES is a crucial issue. The effectiveness of the text is indeed largely based on adequate scientific knowledge of CITES species. This is a major challenge: more than 30,000 species and 178 member states are involved. The issue of expertise, however, is not really addressed by the Convention. The question was left to the consideration of the COP. Therefore, the COP has created two ad hoc scientific committees: the Plants Committee and the Animals Committee, conferring upon them an ambitious mandate. The article addresses some important issues at stake which are linked to institutional questions, as well as the mixed record after twenty-five years of practice.

The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research.

PubMed

Cano, Isaac; Tényi, Ákos; Schueller, Christine; Wolff, Martin; Huertas Migueláñez, M Mercedes; Gomez-Cabrero, David; Antczak, Philipp; Roca, Josep; Cascante, Marta; Falciani, Francesco; Maier, Dieter

2014-11-28

Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov

PubMed Central

Xu, Jun; Lee, Hee-Jin; Zeng, Jia; Wu, Yonghui; Zhang, Yaoyun; Huang, Liang-Chin; Johnson, Amber; Holla, Vijaykumar; Bailey, Ann M; Cohen, Trevor; Meric-Bernstam, Funda; Bernstam, Elmer V

2016-01-01

Objective: Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov. Methods: We developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base. Results and Discussion: The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy. PMID:27013523

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.

PubMed

Xu, Jun; Lee, Hee-Jin; Zeng, Jia; Wu, Yonghui; Zhang, Yaoyun; Huang, Liang-Chin; Johnson, Amber; Holla, Vijaykumar; Bailey, Ann M; Cohen, Trevor; Meric-Bernstam, Funda; Bernstam, Elmer V; Xu, Hua

2016-07-01

Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov. We developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base. The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Knowledge environments representing molecular entities for the virtual physiological human.

PubMed

Hofmann-Apitius, Martin; Fluck, Juliane; Furlong, Laura; Fornes, Oriol; Kolárik, Corinna; Hanser, Susanne; Boeker, Martin; Schulz, Stefan; Sanz, Ferran; Klinger, Roman; Mevissen, Theo; Gattermayer, Tobias; Oliva, Baldo; Friedrich, Christoph M

2008-09-13

In essence, the virtual physiological human (VPH) is a multiscale representation of human physiology spanning from the molecular level via cellular processes and multicellular organization of tissues to complex organ function. The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly. Here, we describe methods and strategies to generate knowledge environments representing molecular entities that can be used for modelling the molecular scale of the VPH. Our strategy to generate knowledge environments representing molecular entities is based on the combination of information extraction from scientific text and the integration of information from biomolecular databases. We introduce @neuLink, a first prototype of an automatically generated, disease-specific knowledge environment combining biomolecular, chemical, genetic and medical information. Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.

OntoGene web services for biomedical text mining.

PubMed

Rinaldi, Fabio; Clematide, Simon; Marques, Hernani; Ellendorff, Tilia; Romacker, Martin; Rodriguez-Esteban, Raul

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

OntoGene web services for biomedical text mining

PubMed Central

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges, with top ranked results in several of them. PMID:25472638

Bridging the guideline implementation gap: a systematic, document-centered approach to guideline implementation.

PubMed

Shiffman, Richard N; Michel, George; Essaihi, Abdelwaheb; Thornquist, Elizabeth

2004-01-01

A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems. This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification. The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system. Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge.

The Relationship of Discourse and Topic Knowledge to Fifth Graders' Writing Performance

ERIC Educational Resources Information Center

Olinghouse, Natalie G.; Graham, Steve; Gillespie, Amy

2015-01-01

This study examined whether discourse and topic knowledge separately predicted the overall quality and the inclusion of basic genre elements in 5th grade students' stories, persuasive papers, and informational text once the other type of knowledge as well as topic interest, spelling, handwriting fluency, length of text, and gender were controlled.…

Identification of histone modifications in biomedical text for supporting epigenomic research

PubMed Central

Kolářik, Corinna; Klinger, Roman; Hofmann-Apitius, Martin

2009-01-01

Background Posttranslational modifications of histones influence the structure of chromatine and in such a way take part in the regulation of gene expression. Certain histone modification patterns, distributed over the genome, are connected to cell as well as tissue differentiation and to the adaption of organisms to their environment. Abnormal changes instead influence the development of disease states like cancer. The regulation mechanisms for modifying histones and its functionalities are the subject of epigenomics investigation and are still not completely understood. Text provides a rich resource of knowledge on epigenomics and modifications of histones in particular. It contains information about experimental studies, the conditions used, and results. To our knowledge, no approach has been published so far for identifying histone modifications in text. Results We have developed an approach for identifying histone modifications in biomedical literature with Conditional Random Fields (CRF) and for resolving the recognized histone modification term variants by term standardization. For the term identification F1 measures of 0.84 by 10-fold cross-validation on the training corpus and 0.81 on an independent test corpus have been obtained. The standardization enabled the correct transformation of 96% of the terms from training and 98% from test the corpus. Due to the lack of terminologies exhaustively covering specific histone modification types, we developed a histone modification term hierarchy for use in a semantic text retrieval system. Conclusion The developed approach highly improves the retrieval of articles describing histone modifications. Since text contains context information about performed studies and experiments, the identification of histone modifications is the basis for supporting literature-based knowledge discovery and hypothesis generation to accelerate epigenomic research. PMID:19208128

Identification of histone modifications in biomedical text for supporting epigenomic research.

PubMed

Kolárik, Corinna; Klinger, Roman; Hofmann-Apitius, Martin

2009-01-30

Posttranslational modifications of histones influence the structure of chromatine and in such a way take part in the regulation of gene expression. Certain histone modification patterns, distributed over the genome, are connected to cell as well as tissue differentiation and to the adaption of organisms to their environment. Abnormal changes instead influence the development of disease states like cancer. The regulation mechanisms for modifying histones and its functionalities are the subject of epigenomics investigation and are still not completely understood. Text provides a rich resource of knowledge on epigenomics and modifications of histones in particular. It contains information about experimental studies, the conditions used, and results. To our knowledge, no approach has been published so far for identifying histone modifications in text. We have developed an approach for identifying histone modifications in biomedical literature with Conditional Random Fields (CRF) and for resolving the recognized histone modification term variants by term standardization. For the term identification F1 measures of 0.84 by 10-fold cross-validation on the training corpus and 0.81 on an independent test corpus have been obtained. The standardization enabled the correct transformation of 96% of the terms from training and 98% from test the corpus. Due to the lack of terminologies exhaustively covering specific histone modification types, we developed a histone modification term hierarchy for use in a semantic text retrieval system. The developed approach highly improves the retrieval of articles describing histone modifications. Since text contains context information about performed studies and experiments, the identification of histone modifications is the basis for supporting literature-based knowledge discovery and hypothesis generation to accelerate epigenomic research.

Text mining for traditional Chinese medical knowledge discovery: a survey.

PubMed

Zhou, Xuezhong; Peng, Yonghong; Liu, Baoyan

2010-08-01

Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions. Copyright 2010 Elsevier Inc. All rights reserved.

eVoc Strategies: 10 Ways to Use Technology to Build Vocabulary

ERIC Educational Resources Information Center

Dalton, Bridget; Grisham, Dana L.

2011-01-01

Vocabulary knowledge is key to comprehension and expression. For students in the intermediate grades, the need for breadth and depth of vocabulary accelerates as they encounter more challenging academic texts in print and on the Internet. Drawing on research-based principles of vocabulary instruction and multimedia learning, this article presents…

English Perceptive Teaching of Middle School in China--Based on an Empirical Study

ERIC Educational Resources Information Center

Lifen, He; Junying, Yong

2016-01-01

Perception is the reconstruction and interaction between the new information and prior knowledge in mind or in the process of internalization about the new information. It has three teaching procedures: First, teachers elicit the learners to acquire text meaning. Second, teachers create situation in practical teaching. Third, learners comprehend…

Growing Reading Fluency: Engaging Readers with Technology and Text

ERIC Educational Resources Information Center

Parenti, Melissa A.; Chen, Xiaojun

2015-01-01

The presence of technology in K-12 classrooms continues to increase. With the onset of these technological advances, a refined lens for analysis of the effectiveness of these tools is required. Web based tools necessitate a synthesis of Technological, Pedagogical and Content knowledge. Moreover, the use of technology should support the content and…

Predicting Lexical Proficiency in Language Learner Texts Using Computational Indices

ERIC Educational Resources Information Center

Crossley, Scott A.; Salsbury, Tom; McNamara, Danielle S.; Jarvis, Scott

2011-01-01

The authors present a model of lexical proficiency based on lexical indices related to vocabulary size, depth of lexical knowledge, and accessibility to core lexical items. The lexical indices used in this study come from the computational tool Coh-Metrix and include word length scores, lexical diversity values, word frequency counts, hypernymy…

Precise and Efficient Retrieval of Captioned Images: The MARIE Project.

ERIC Educational Resources Information Center

Rowe, Neil C.

1999-01-01

The MARIE project explores knowledge-based information retrieval of captioned images of the kind found in picture libraries and on the Internet. MARIE's five-part approach exploits the idea that images are easier to understand with context, especially descriptive text near them, but it also does image analysis. Experiments show MARIE prototypes…

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach.

PubMed

Mouriño García, Marcos Antonio; Pérez Rodríguez, Roberto; Anido Rifón, Luis E

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria-that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text-thus suffering from synonymy and polysemy-and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge-concretely Wikipedia-in order to create bag-of-concepts (BoC) representations of documents, understanding concept as "unit of meaning", and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus.

Examining the Influences of Epistemic Beliefs and Knowledge Representations on Cognitive Processing and Conceptual Change When Learning Physics

ERIC Educational Resources Information Center

Franco, Gina M.; Muis, Krista R.; Kendeou, Panayiota; Ranellucci, John; Sampasivam, Lavanya; Wang, Xihui

2012-01-01

The purpose of this study was to investigate the role of epistemic beliefs and knowledge representations in cognitive and metacognitive processing when learning about physics concepts through text. Specifically, we manipulated the representation of physics concepts in texts about Newtonian mechanics and explored how these texts interacted with…

The Case for Multiple Texts

ERIC Educational Resources Information Center

Cummins, Sunday

2017-01-01

Reading just one text on any topic, Cummins argues, isn't enough if we expect students to learn at deep levels about the topic, synthesize various sources of information, and gain the knowledge they need to write and speak seriously about the topic. Reading a second or third text expands a reader's knowledge on any topic or story--and the why…

Undergraduate Students' Ability to Revise Text Effectively: Relationships with Topic Knowledge and Working Memory

ERIC Educational Resources Information Center

Adams, Anne-Marie; Simmons, Fiona; Willis, Catherine; Pawling, Ralph

2010-01-01

In order to communicate understanding, students are often required to produce texts which present an explicit, coherent argument. This study examined the extent to which individual differences in undergraduates' topic knowledge and working memory skills were related to their ability to revise texts to better fulfil these goals. Forty-seven…

The Contribution of Knowledge about Anaphors, Organisational Signals and Refutations to Reading Comprehension

ERIC Educational Resources Information Center

García, J. Ricardo; Bustos, Andrea; Sánchez, Emilio

2015-01-01

Expository texts contain rhetorical devices that help readers to connect text ideas (within a text and with prior knowledge) and to monitor reading. Rhetorical competence addresses readers' skill in detecting, understanding and using these devices. We examined the contribution of rhetorical competence to reading comprehension on two groups of 11-…

Toward translational incremental similarity-based reasoning in breast cancer grading

NASA Astrophysics Data System (ADS)

Tutac, Adina E.; Racoceanu, Daniel; Leow, Wee-Keng; Müller, Henning; Putti, Thomas; Cretu, Vladimir

2009-02-01

One of the fundamental issues in bridging the gap between the proliferation of Content-Based Image Retrieval (CBIR) systems in the scientific literature and the deficiency of their usage in medical community is based on the characteristic of CBIR to access information by images or/and text only. Yet, the way physicians are reasoning about patients leads intuitively to a case representation. Hence, a proper solution to overcome this gap is to consider a CBIR approach inspired by Case-Based Reasoning (CBR), which naturally introduces medical knowledge structured by cases. Moreover, in a CBR system, the knowledge is incrementally added and learned. The purpose of this study is to initiate a translational solution from CBIR algorithms to clinical practice, using a CBIR/CBR hybrid approach. Therefore, we advance the idea of a translational incremental similarity-based reasoning (TISBR), using combined CBIR and CBR characteristics: incremental learning of medical knowledge, medical case-based structure of the knowledge (CBR), image usage to retrieve similar cases (CBIR), similarity concept (central for both paradigms). For this purpose, three major axes are explored: the indexing, the cases retrieval and the search refinement, applied to Breast Cancer Grading (BCG), a powerful breast cancer prognosis exam. The effectiveness of this strategy is currently evaluated over cases provided by the Pathology Department of Singapore National University Hospital, for the indexing. With its current accuracy, TISBR launches interesting perspectives for complex reasoning in future medical research, opening the way to a better knowledge traceability and a better acceptance rate of computer-aided diagnosis assistance among practitioners.

Effects of Reading Strategies and Depth of Vocabulary Knowledge on Turkish EFL Learners' Text Inferencing Skills

ERIC Educational Resources Information Center

Çakir, Abdulvahit; Ünaldi, Ihsan; Arslan, Fadime Yalçin; Kiliç, Mehmet

2016-01-01

Within the framework of foreign language teaching and learning, reading strategies, depth of vocabulary knowledge and text inferencing skills have not been researched extensively. This study tries to fill this gap by analyzing the effects of reading strategies used by Turkish EFL learners and their depth of vocabulary knowledge on their text…

Self-Assessment of Word Knowledge with Graded Readers: A Preliminary Study

ERIC Educational Resources Information Center

Wan-a-rom, Udorn

2010-01-01

The study investigated how second language (L2) learners self-assessed word knowledge on a page of text taken from a graded reader. The case study subjects were five Thai high school learners of English. They were asked to assess their word knowledge using a page of continuous text. Data gained through observation, interviews, self-assessment and…

Local Semantic Trace: A Method to Analyze Very Small and Unstructured Texts for Propositional Knowledge

ERIC Educational Resources Information Center

Pirnay-Dummer, Pablo

2015-01-01

A local semantic trace is a certain quasi-propositional structure that can still be reconstructed from written content that is incomplete or does not follow a proper grammar. It can also retrace bits of knowledge from text containing only very few words, making the microstructure of these artifacts of knowledge externalization available for…

The Impact of Secondary School Students' Preconceptions on the Evolution of their Mental Models of the Greenhouse effect and Global Warming

NASA Astrophysics Data System (ADS)

Reinfried, Sibylle; Tempelmann, Sebastian

2014-01-01

This paper provides a video-based learning process study that investigates the kinds of mental models of the atmospheric greenhouse effect 13-year-old learners have and how these mental models change with a learning environment, which is optimised in regard to instructional psychology. The objective of this explorative study was to observe and analyse the learners' learning pathways according to their previous knowledge in detail and to understand the mental model formation processes associated with them more precisely. For the analysis of the learning pathways, drawings, texts, video and interview transcripts from 12 students were studied using qualitative methods. The learning pathways pursued by the learners significantly depend on their domain-specific previous knowledge. The learners' preconceptions could be typified based on specific characteristics, whereby three preconception types could be formed. The 'isolated pieces of knowledge' type of learners, who have very little or no previous knowledge about the greenhouse effect, build new mental models that are close to the target model. 'Reduced heat output' type of learners, who have previous knowledge that indicates compliances with central ideas of the normative model, reconstruct their knowledge by reorganising and interpreting their existing knowledge structures. 'Increasing heat input' type of learners, whose previous knowledge consists of subjective worldly knowledge, which has a greater personal explanatory value than the information from the learning environment, have more difficulties changing their mental models. They have to fundamentally reconstruct their mental models.

Group Theory with Applications in Chemical Physics

NASA Astrophysics Data System (ADS)

Jacobs, Patrick

2005-10-01

Group Theory is an indispensable mathematical tool in many branches of chemistry and physics. This book provides a self-contained and rigorous account on the fundamentals and applications of the subject to chemical physics, assuming no prior knowledge of group theory. The first half of the book focuses on elementary topics, such as molecular and crystal symmetry, whilst the latter half is more advanced in nature. Discussions on more complex material such as space groups, projective representations, magnetic crystals and spinor bases, often omitted from introductory texts, are expertly dealt with. With the inclusion of numerous exercises and worked examples, this book will appeal to advanced undergraduates and beginning graduate students studying physical sciences and is an ideal text for use on a two-semester course. An introductory and advanced text that comprehensively covers fundamentals and applications of group theory in detail Suitable for a two-semester course with numerous worked examples and problems Includes several topics often omitted from introductory texts, such as rotation group, space groups and spinor bases

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

PubMed

Maier, Dieter; Kalus, Wenzel; Wolff, Martin; Kalko, Susana G; Roca, Josep; Marin de Mas, Igor; Turan, Nil; Cascante, Marta; Falciani, Francesco; Hernandez, Miguel; Villà-Freixa, Jordi; Losko, Sascha

2011-03-05

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

PubMed Central

2011-01-01

Background To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development. PMID:21375767

Inferring Facts From Fiction: Reading Correct and Incorrect Information Affects Memory for Related Information

PubMed Central

Butler, Andrew C.; Dennis, Nancy A.; Marsh, Elizabeth J.

2012-01-01

People can acquire both true and false knowledge about the world from fictional stories (Marsh & Fazio, 2007). The present study explored whether the benefits and costs of learning about the world from fictional stories extend beyond memory for directly stated pieces of information. Of interest was whether readers would use correct and incorrect story references to make deductive inferences about related information in the story, and then integrate those inferences into their knowledge bases. Subjects read stories containing correct, neutral, and misleading references to facts about the world; each reference could be combined with another reference that occurred in a later sentence to make a deductive inference. Later, they answered general knowledge questions that tested for these deductive inferences. The results showed that subjects generated and retained the deductive inferences regardless of whether the inferences were consistent or inconsistent with world knowledge, and irrespective of whether the references were placed consecutively in the text or separated by many sentences. Readers learn more than what is directly stated in stories; they use references to the real world to make both correct and incorrect inferences that are integrated into their knowledge bases. PMID:22640369

Differences among Major Taxa in the Extent of Ecological Knowledge across Four Major Ecosystems

PubMed Central

Fisher, Rebecca; Knowlton, Nancy; Brainard, Russell E.; Caley, M. Julian

2011-01-01

Existing knowledge shapes our understanding of ecosystems and is critical for ecosystem-based management of the world's natural resources. Typically this knowledge is biased among taxa, with some taxa far better studied than others, but the extent of this bias is poorly known. In conjunction with the publically available World Registry of Marine Species database (WoRMS) and one of the world's premier electronic scientific literature databases (Web of Science®), a text mining approach is used to examine the distribution of existing ecological knowledge among taxa in coral reef, mangrove, seagrass and kelp bed ecosystems. We found that for each of these ecosystems, most research has been limited to a few groups of organisms. While this bias clearly reflects the perceived importance of some taxa as commercially or ecologically valuable, the relative lack of research of other taxonomic groups highlights the problem that some key taxa and associated ecosystem processes they affect may be poorly understood or completely ignored. The approach outlined here could be applied to any type of ecosystem for analyzing previous research effort and identifying knowledge gaps in order to improve ecosystem-based conservation and management. PMID:22073172

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

PubMed

Duque, Andres; Stevenson, Mark; Martinez-Romo, Juan; Araujo, Lourdes

2018-05-01

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain. Unlike other unsupervised approaches based on static graphs such as UMLS, in this work the knowledge base takes the context of the ambiguous terms into account. Abstracts downloaded from PubMed are used for building the graph and disambiguation is performed using the personalized PageRank algorithm. Evaluation is carried out over two test datasets widely explored in the literature. Different parameters of the system are also evaluated to test robustness and scalability. Results show that the system is able to outperform state-of-the-art knowledge-based systems, obtaining more than 10% of accuracy improvement in some cases, while only requiring minimal external resources. Copyright © 2018 Elsevier B.V. All rights reserved.

Systems of evidence-based healthcare and personalised health information: some international and national trends.

PubMed

Gordon, C; Gray, J A; Toth, B; Veloso, M

2000-01-01

In Europe, North America and elsewhere, growing interest has focussed on evidence-based healthcare systems, incorporating the deployment of practice guidelines, as a field of application for health telematics. The clinical benefit and technical feasibility of common European approaches to this task has recently been demonstrated. In Europe it is likely that, building on recent progress in electronic health record architecture (EHRA) standards, a sufficient state of maturity can be reached to justify initiation within CEN TC251 of a prestandards process on guideline content formats during the current 5th Framework of EC RT&D activity. There is now a similar impetus to agree standards for this field in North America. Thanks to fruitful EC-USA contacts during the 4th Framework programme, there is now a chance, given well-planned coordination, to establish a global consensus optimally suited to serve the world-wide delivery and application of evidence-based medicine. This review notes three factors which may accelerate progress to convergence: (1) revolutionary changes in the knowledge basis of professional/patient/public healthcare partnerships, involving the key role of the Web as a health knowledge resource for citizens, and a rapidly growing market for personalised health information and advice; (2) the emergence at national levels of digital warehouses of clinical guidelines and EBM knowledge resources, agencies which are capable of brokering common mark-up and interchange media definitions between knowledge providers, industry and healthcare organizations; (3) the closing gap in knowledge management technology, with the advent of XML and RDF, between approaches and services based respectively on text mark-up and knowledge-base paradigms. A current project in the UK National Health Service (the National electronic Library of Health) is cited as an example of a national initiative designed to harness these trends.

The Contribution of Vocabulary Knowledge and Semantic Orthographic Fluency to Text Quality through Elementary School in Catalan

ERIC Educational Resources Information Center

Castillo, Cristina; Tolchinsky, Liliana

2018-01-01

Building a text is a multidimensional endeavor. Writers must work simultaneously on the content of the text, its discursive organization, the structure of the sentences, and the individual words themselves. Knowledge of vocabulary is central to this endeavor. This study intends (1) to trace the development of writer's vocabulary depth, their…

Exploring Pre-Service Teachers' Knowledge of and Ability to Use Text Messaging

ERIC Educational Resources Information Center

Geng, Gretchen; Disney, Leigh

2014-01-01

This study aimed to assess the pre-service teachers' knowledge of and ability to use text messaging, and assist their use of this technology in the classroom teaching context. Data were gathered by means of a questionnaire and text message exercises. Fifty-three pre-service teachers participated in the study. It was found that although different…

Training Inference Making Skills Using a Situation Model Approach Improves Reading Comprehension

PubMed Central

Bos, Lisanne T.; De Koning, Bjorn B.; Wassenburg, Stephanie I.; van der Schoot, Menno

2016-01-01

This study aimed to enhance third and fourth graders’ text comprehension at the situation model level. Therefore, we tested a reading strategy training developed to target inference making skills, which are widely considered to be pivotal to situation model construction. The training was grounded in contemporary literature on situation model-based inference making and addressed the source (text-based versus knowledge-based), type (necessary versus unnecessary for (re-)establishing coherence), and depth of an inference (making single lexical inferences versus combining multiple lexical inferences), as well as the type of searching strategy (forward versus backward). Results indicated that, compared to a control group (n = 51), children who followed the experimental training (n = 67) improved their inference making skills supportive to situation model construction. Importantly, our training also resulted in increased levels of general reading comprehension and motivation. In sum, this study showed that a ‘level of text representation’-approach can provide a useful framework to teach inference making skills to third and fourth graders. PMID:26913014

[Application of text mining approach to pre-education prior to clinical practice].

PubMed

Koinuma, Masayoshi; Koike, Katsuya; Nakamura, Hitoshi

2008-06-01

We developed a new survey analysis technique to understand students' actual aims for effective pretraining prior to clinical practice. We asked third-year undergraduate students to write fixed-style complete and free sentences on "preparation of drug dispensing." Then, we converted their sentence data in to text style and performed Japanese-language morphologic analysis on the data using language analysis software. We classified key words, which were created on the basis of the word class information of the Japanese language morphologic analysis, into categories based on causes and characteristics. In addition to this, we classified the characteristics into six categories consisting of those concepts including "knowledge," "skill and attitude," "image," etc. with the KJ method technique. The results showed that the awareness of students of "preparation of drug dispensing" tended to be approximately three-fold more frequent in "skill and attitude," "risk," etc. than in "knowledge." Regarding the characteristics in the category of the "image," words like "hard," "challenging," "responsibility," "life," etc. frequently occurred. The results of corresponding analysis showed that the characteristics of the words "knowledge" and "skills and attitude" were independent. As the result of developing a cause-and-effect diagram, it was demonstrated that the phase "hanging tough" described most of the various factors. We thus could understand students' actual feelings by applying text-mining as a new survey analysis technique.

From Data to Knowledge – Promising Analytical Tools and Techniques for Capture and Reuse of Corporate Knowledge and to Aid in the State Evaluation Process

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

Danielson, Gary R.; Augustenborg, Elsa C.; Beck, Andrew E.

2010-10-29

The IAEA is challenged with limited availability of human resources for inspection and data analysis while proliferation threats increase. PNNL has a variety of IT solutions and techniques (at varying levels of maturity and development) that take raw data closer to useful knowledge, thereby assisting with and standardizing the analytical processes. This paper highlights some PNNL tools and techniques which are applicable to the international safeguards community, including: • Intelligent in-situ triage of data prior to reliable transmission to an analysis center resulting in the transmission of smaller and more relevant data sets • Capture of expert knowledge in re-usablemore » search strings tailored to specific mission outcomes • Image based searching fused with text based searching • Use of gaming to discover unexpected proliferation scenarios • Process modeling (e.g. Physical Model) as the basis for an information integration portal, which links to data storage locations along with analyst annotations, categorizations, geographic data, search strings and visualization outputs.« less

Continuity and change in medicinal plant use: the example of monasteries on Cyprus and historical iatrosophia texts.

PubMed

Lardos, Andreas; Heinrich, Michael

2013-10-28

How medicinal plant knowledge changes over time is a question of central importance in modern ethnopharmacological research. However, only few studies are available which undertook a comprehensive exploration of the evolution of plant use in human cultures. In order to understand this dynamic process, we conduct a systematic diachronic investigation to explore continuity and change in two knowledge systems which are closely related but separated in time-historical iatrosophia texts and today's Greek Orthodox monasteries on Cyprus. An ethnobotanical study was conducted in 21 of the island's monasteries involving various types of interview as well as a written questionnaire survey. Data about medicinal plant use collected in the monasteries was analysed and quantitatively compared to historical iatrosophia texts using data from our pre-existing dataset. We found a core group of plant taxa for which a high consensus exists among the monasteries regarding their medicinal usefulness. Various means and routes of knowledge transmission appear to be involved in the development of this knowledge. The systematic comparison between the monasteries and the iatrosophia shows similarities and differences on various levels. While the plants used by the nuns and monks have by the majority a relationship to the iatrosophia and show a remarkable historical consistency in terms of their use for defined groups of ailments, the importance of many of these plants and the use of herbal medicines in general have changed. This is one of the first studies from the Mediterranean region which is based on a systematic ethnopharmacological analysis involving comprehensive datasets of historical and modern ethnographic data. The example illustrates continuity and change in 'traditional' knowledge as well as the adoption of new knowledge and provides the opportunity to look beyond the dichotomy between traditional and modern concepts of plant usage. Overall, the study suggests that a systematic diachronic approach can facilitate a better understanding of the complex and dynamic processes involved in the development of medicinal plant knowledge. © 2013 Elsevier Ireland Ltd. All rights reserved.

Comprehending Comprehension: Selected Possibilities for Clinical Practice Within a Multidimensional Model.

PubMed

Wallach, Geraldine P; Ocampo, Alaine

2017-04-20

In this discussion as part of a response to Catts and Kamhi's "Prologue: Reading Comprehension Is Not a Single Activity" (2017), the authors provide selected examples from 4th-, 5th-, and 6th-grade texts to demonstrate, in agreement with Catts and Kamhi, that reading comprehension is a multifaceted and complex ability. The authors were asked to provide readers with evidence-based practices that lend support to applications of a multidimensional model of comprehension. We present examples from the reading comprehension literature that support the notion that reading is a complex set of abilities that include a reader's ability, especially background knowledge; the type of text the reader is being asked to comprehend; and the task or technique used in assessment or intervention paradigms. An intervention session from 6th grade serves to demonstrate how background knowledge, a text's demands, and tasks may come together in the real world as clinicians and educators aim to help students comprehend complex material. The authors agree with the conceptual framework proposed by Catts and Kamhi that clinicians and educators should consider the multidimensional nature of reading comprehension (an interaction of reader, text, and task) when creating assessment and intervention programs. The authors might depart slightly by considering, more closely, those reading comprehension strategies that might facilitate comprehension across texts and tasks with an understanding of students' individual needs at different points in time.

The power and limits of a rule-based morpho-semantic parser.

PubMed Central

Baud, R. H.; Rassinoux, A. M.; Ruch, P.; Lovis, C.; Scherrer, J. R.

1999-01-01

The venue of Electronic Patient Record (EPR) implies an increasing amount of medical texts readily available for processing, as soon as convenient tools are made available. The chief application is text analysis, from which one can drive other disciplines like indexing for retrieval, knowledge representation, translation and inferencing for medical intelligent systems. Prerequisites for a convenient analyzer of medical texts are: building the lexicon, developing semantic representation of the domain, having a large corpus of texts available for statistical analysis, and finally mastering robust and powerful parsing techniques in order to satisfy the constraints of the medical domain. This article aims at presenting an easy-to-use parser ready to be adapted in different settings. It describes its power together with its practical limitations as experienced by the authors. PMID:10566313

The power and limits of a rule-based morpho-semantic parser.

PubMed

Baud, R H; Rassinoux, A M; Ruch, P; Lovis, C; Scherrer, J R

1999-01-01

The venue of Electronic Patient Record (EPR) implies an increasing amount of medical texts readily available for processing, as soon as convenient tools are made available. The chief application is text analysis, from which one can drive other disciplines like indexing for retrieval, knowledge representation, translation and inferencing for medical intelligent systems. Prerequisites for a convenient analyzer of medical texts are: building the lexicon, developing semantic representation of the domain, having a large corpus of texts available for statistical analysis, and finally mastering robust and powerful parsing techniques in order to satisfy the constraints of the medical domain. This article aims at presenting an easy-to-use parser ready to be adapted in different settings. It describes its power together with its practical limitations as experienced by the authors.

Embedding Open-domain Common-sense Knowledge from Text

PubMed Central

Goodwin, Travis; Harabagiu, Sanda

2017-01-01

Our ability to understand language often relies on common-sense knowledge – background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language. PMID:28649676

The Construction of Teaching Model on College English Writing from the Perspective of Cognitive Genre

ERIC Educational Resources Information Center

Wenjuan, Hao; Rui, Liang

2016-01-01

Teaching is a spiral rising process. A complete teaching should be composed of five parts: theoretical basis, goal orientation, operating procedures, implementation conditions and assessment. On the basis of the genre knowledge, content-based approach and process approach, this text constructs the Teaching Model of College Writing Instruction, in…

Student Connections with Academic Texts: A Phenomenographic Study of Reading

ERIC Educational Resources Information Center

MacMillan, Margy

2014-01-01

Concerns about the ability of post-secondary students to read scholarly materials are well documented in the literature. A key aspect of reading at the deeper level expected of these students is connecting new information to prior knowledge. This study is based on an activity where students were explicitly required to make such connections as part…

Literary Translation, Translating Culture: The Case of Shahriyar, the Famous Iranian Azeri Poet

ERIC Educational Resources Information Center

Kianbakht, Saijad

2016-01-01

A literary translation is a device of art used to release the text from its dependence on prior cultural knowledge (Herzfeld, 2003). The present research investigates the use of pragmatic equivalence in two translations of the Azeri Turkish long poem "Haydar Babaye Salam" by "Shahriyar." Based on Koller's theory of equivalence…

Robust Deep Semantics for Language Understanding

DTIC Science & Technology

focus on five areas: deep learning, textual inferential relations, relation and event extraction by distant supervision , semantic parsing and...ontology expansion, and coreference resolution. As time went by, the program focus converged towards emphasizing technologies for knowledge base...natural logic methods for text understanding, improved mention coreference algorithms, and the further development of multilingual tools in CoreNLP.

Reinventing the High School Government Course: Rigor, Simulations, and Learning from Text

ERIC Educational Resources Information Center

Parker, Walter C.; Lo, Jane C.

2016-01-01

The high school government course is arguably the main site of formal civic education in the country today. This article presents the curriculum that resulted from a multiyear study aimed at improving the course. The pedagogic model, called "Knowledge in Action," centers on a rigorous form of project-based learning where the projects are…

Drawing Dynamic Geometry Figures Online with Natural Language for Junior High School Geometry

ERIC Educational Resources Information Center

Wong, Wing-Kwong; Yin, Sheng-Kai; Yang, Chang-Zhe

2012-01-01

This paper presents a tool for drawing dynamic geometric figures by understanding the texts of geometry problems. With the tool, teachers and students can construct dynamic geometric figures on a web page by inputting a geometry problem in natural language. First we need to build the knowledge base for understanding geometry problems. With the…

Enhancing Pre-Service Elementary Teachers' Conceptual Understanding of Solution Chemistry with Conceptual Change Text

ERIC Educational Resources Information Center

Calik, Muammer; Ayas, Alipasa; Coll, Richard Kevin

2007-01-01

This paper reports on the use of a constructivist-based pedagogy to enhance understanding of some features of solution chemistry. Pre-service science teacher trainees' prior knowledge about the dissolution of salts and sugar in water were elicited by the use of a simple diagnostic tool. The test revealed widespread alternative conceptions. These…

Differentiating Digital Writing Instruction: The Intersection of Technology, Writing Instruction, and Digital Genre Knowledge

ERIC Educational Resources Information Center

Martin, Nicole M.; Lambert, Claire

2015-01-01

U.S. adolescents' prior technology experiences and exposure to digital genres vary, but they will often write digital texts as they enter college and adulthood. We explored middle school students' digital writing instructional experience in the context of a university-based summer digital writing camp. The sixth- through eighth-grade adolescents…

Extending E-Book with Contextual Knowledge Recommender for Reading Support on a Web-Based Learning System

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Wei, Fu-Hsiang; Wang, Chin-Yeh; Lee, Jih-Hsien

2007-01-01

Reading content of the Web is increasingly popular. When students read the same material, each student has a unique comprehension of the text and requires individual support from appropriate references. Most references in typical web learning systems are unorganized. Students are often required to disrupt their reading to locate references. This…

Models of Vocabulary Acquisition: Direct Tests and Text-Derived Simulations of Vocabulary Growth

ERIC Educational Resources Information Center

Biemiller, Andrew; Rosenstein, Mark; Sparks, Randall; Landauer, Thomas K.; Foltz, Peter W.

2014-01-01

Determining word meanings that ought to be taught or introduced is important for educators. A sequence for vocabulary growth can be inferred from many sources, including testing children's knowledge of word meanings at various ages, predicting from print frequency, or adult-recalled Age of Acquisition. A new approach, Word Maturity, is based on…

Dewey's Logic as a Methodological Grounding Point for Practitioner-Based Inquiry

ERIC Educational Resources Information Center

Demetrion, George

2012-01-01

The purpose of this essay is to draw out key insights from Dewey's important text "Logic: The Theory of Inquiry" to provide theoretical and practical support for the emergent field of teacher research. The specific focal point is the argument in Cochran-Smith and Lytle's "Inside/Outside: Teacher Research and Knowledge" on the significance of…

The Effect of Images on Item Statistics in Multiple Choice Anatomy Examinations

ERIC Educational Resources Information Center

Notebaert, Andrew J.

2017-01-01

Although multiple choice examinations are often used to test anatomical knowledge, these often forgo the use of images in favor of text-based questions and answers. Because anatomy is reliant on visual resources, examinations using images should be used when appropriate. This study was a retrospective analysis of examination items that were text…

Bridging the Guideline Implementation Gap: A Systematic, Document-Centered Approach to Guideline Implementation

PubMed Central

Shiffman, Richard N.; Michel, George; Essaihi, Abdelwaheb; Thornquist, Elizabeth

2004-01-01

Objective: A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems. Design: This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification. Results: The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system. Conclusion: Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge. PMID:15187061

A randomized controlled trial of the impact of a family planning mHealth service on knowledge and use of contraception.

PubMed

Johnson, Douglas; Juras, Randall; Riley, Pamela; Chatterji, Minki; Sloane, Phoebe; Choi, Soon Kyu; Johns, Ben

2017-01-01

mHealth, or the use of mobile phones for health, is a promising but largely untested method for increasing family planning knowledge in developing countries. This study estimates the effect of m4RH, an mHealth service in Kenya that provides family planning information via text message, on consumers' knowledge and use of contraception. We randomly assigned new consumers of the m4RH service to receive either full access or limited access to m4RH. We collected data on outcomes by sending questions directly to consumers via text message. Response rates to the text message surveys ranged from 51.8% to 13.5%. Despite relatively low response rates, response rates were very similar across the full-access and limited-access groups. We find that full access to m4RH increased consumers' scores on a test of contraceptive knowledge by 14% (95% confidence interval: 9.9%-18.2%) compared to a control group with limited access to m4RH. m4RH did not increase consumers' use of contraception, likelihood of discussing family planning with their partners, or likelihood of visiting a clinic to discuss family planning. Text messages may increase family planning knowledge but do not, by themselves, lead to behavior change. Text messages can be an effective method of increasing family planning knowledge but may be insufficient on their own to cause behavior change. Copyright © 2016 Elsevier Inc. All rights reserved.

KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences.

PubMed

Ernst, Patrick; Siu, Amy; Weikum, Gerhard

2015-05-14

Biomedical knowledge bases (KB's) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB's based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors. KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from different text genres such as scientific publications, health portals, and online communities. Thus, it has the potential to serve as one-stop portal for a wide range of relations and use cases. To showcase the breadth and usefulness, we make the KnowLife KB accessible through the health portal (http://knowlife.mpi-inf.mpg.de).

Knowledge representation and management: transforming textual information into useful knowledge.

PubMed

Rassinoux, A-M

2010-01-01

To summarize current outstanding research in the field of knowledge representation and management. Synopsis of the articles selected for the IMIA Yearbook 2010. Four interesting papers, dealing with structured knowledge, have been selected for the section knowledge representation and management. Combining the newest techniques in computational linguistics and natural language processing with the latest methods in statistical data analysis, machine learning and text mining has proved to be efficient for turning unstructured textual information into meaningful knowledge. Three of the four selected papers for the section knowledge representation and management corroborate this approach and depict various experiments conducted to .extract meaningful knowledge from unstructured free texts such as extracting cancer disease characteristics from pathology reports, or extracting protein-protein interactions from biomedical papers, as well as extracting knowledge for the support of hypothesis generation in molecular biology from the Medline literature. Finally, the last paper addresses the level of formally representing and structuring information within clinical terminologies in order to render such information easily available and shareable among the health informatics community. Delivering common powerful tools able to automatically extract meaningful information from the huge amount of electronically unstructured free texts is an essential step towards promoting sharing and reusability across applications, domains, and institutions thus contributing to building capacities worldwide.

ADO: a disease ontology representing the domain knowledge specific to Alzheimer's disease.

PubMed

Malhotra, Ashutosh; Younesi, Erfan; Gündel, Michaela; Müller, Bernd; Heneka, Michael T; Hofmann-Apitius, Martin

2014-03-01

Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain. Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format. ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios. Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Semantator: semantic annotator for converting biomedical text to linked data.

PubMed

Tao, Cui; Song, Dezhao; Sharma, Deepak; Chute, Christopher G

2013-10-01

More than 80% of biomedical data is embedded in plain text. The unstructured nature of these text-based documents makes it challenging to easily browse and query the data of interest in them. One approach to facilitate browsing and querying biomedical text is to convert the plain text to a linked web of data, i.e., converting data originally in free text to structured formats with defined meta-level semantics. In this paper, we introduce Semantator (Semantic Annotator), a semantic-web-based environment for annotating data of interest in biomedical documents, browsing and querying the annotated data, and interactively refining annotation results if needed. Through Semantator, information of interest can be either annotated manually or semi-automatically using plug-in information extraction tools. The annotated results will be stored in RDF and can be queried using the SPARQL query language. In addition, semantic reasoners can be directly applied to the annotated data for consistency checking and knowledge inference. Semantator has been released online and was used by the biomedical ontology community who provided positive feedbacks. Our evaluation results indicated that (1) Semantator can perform the annotation functionalities as designed; (2) Semantator can be adopted in real applications in clinical and transactional research; and (3) the annotated results using Semantator can be easily used in Semantic-web-based reasoning tools for further inference. Copyright © 2013 Elsevier Inc. All rights reserved.

Knowledge-based identification of the ERK2/STAT3 signal pathway as a therapeutic target for type 2 diabetes and drug discovery.

PubMed

Kinoshita, Takayoshi; Doi, Kentaro; Sugiyama, Hajime; Kinoshita, Shuhei; Wada, Mutsuyo; Naruto, Shuji; Tomonaga, Atsushi

2011-09-01

Many existing agents for diabetes therapy are unable to restore or maintain normal glucose homeostasis or prevent the eventual emergence of hyperglycemia-related complication. Therefore, agents based on novel mechanisms are sought to complement and extend the current therapeutic approaches. Based on the initial paper research, we focused on active STAT3 as an attractive pharmacological target for type 2 diabetes. The subsequent text mining with a unique query to identify suppressors but not activators of STAT3 revealed the ERK2/STAT3 pathway as a novel diabetes target. The description of ERK2 inhibitors as diabetes target had not been found in our text mining research at present. The mechanism-based peptide inhibitor for ERK2 was identified using the knowledge of the KIM sequence, which has an important role in the recognition of cognate kinases, phosphatases, scaffold proteins, and substrates. The peptide inhibitor was confirmed to exert effects in vitro and in vivo. The peptide inhibitor conferred a significant decrease in HOMA-IR levels on Day 28 compared with that in the vehicle group. Besides lowering the fasting blood glucose level, the peptide inhibitor also attenuated the blood glucose increment in the fed state, as compared with the vehicle group. © 2011 John Wiley & Sons A/S.

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

NASA Astrophysics Data System (ADS)

Candra Permana, Fahmi; Rosmansyah, Yusep; Setiawan Abdullah, Atje

2017-10-01

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.

Novel grid-based optical Braille conversion: from scanning to wording

NASA Astrophysics Data System (ADS)

Yoosefi Babadi, Majid; Jafari, Shahram

2011-12-01

Grid-based optical Braille conversion (GOBCO) is explained in this article. The grid-fitting technique involves processing scanned images taken from old hard-copy Braille manuscripts, recognising and converting them into English ASCII text documents inside a computer. The resulted words are verified using the relevant dictionary to provide the final output. The algorithms employed in this article can be easily modified to be implemented on other visual pattern recognition systems and text extraction applications. This technique has several advantages including: simplicity of the algorithm, high speed of execution, ability to help visually impaired persons and blind people to work with fax machines and the like, and the ability to help sighted people with no prior knowledge of Braille to understand hard-copy Braille manuscripts.

Consumer understanding of calorie labeling: a healthy monday e-mail and text message intervention.

PubMed

Abel, Michelle L; Lee, Katherine; Loglisci, Ralph; Righter, Allison; Hipper, Thomas J; Cheskin, Lawrence J

2015-03-01

To assess caloric knowledge of participants and determine if an e-mail and/or text message intervention could increase knowledge of recommended daily caloric intake. Randomized, control trial. Johns Hopkins Hospital Cobblestone Café. The 246 participants reported eating at the Café at least twice/week. Participants randomized to control, e-mail, or text condition. The text and e-mail conditions received a message on four consecutive Mondays stating the recommended daily caloric intake. Knowledge of the government reference value of 2,000 calories. Intention-to-treat analysis was conducted. Multivariate logistic regression examined the effectiveness of text and e-mail messaging for improving knowledge of the government calorie reference value. Baseline awareness of the daily calorie reference value in study population was low. Participants in the text message condition were twice as likely to know the government calorie reference value compared to controls (p = .047, odds ratio = 2.2, 95% confidence interval [1.01, 4.73]). No significant differences were found for the e-mail condition (p = .5). Many people do not know the daily recommended caloric intake. Public education on the government calorie reference value is necessary for menu-labeling interventions to be more effective. Weekly text messaging can serve as an effective modality for delivering calorie information and nutrition education. © 2014 Society for Public Health Education.

A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

PubMed

Jiang, Guoqian; Wang, Chen; Zhu, Qian; Chute, Christopher G

2013-01-01

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

Knowing Who Knows: Laypersons' Capabilities to Judge Experts' Pertinence for Science Topics.

PubMed

Bromme, Rainer; Thomm, Eva

2016-01-01

Because modern societies are built on elaborate divisions of cognitive labor, individuals remain laypersons in most knowledge domains. Hence, they have to rely on others' expertise when deciding on many science-related issues in private and public life. Even children already locate and discern expertise in the minds of others (e.g., Danovitch & Keil, 2004). This study examines how far university students accurately judge experts' pertinence for science topics even when they lack proficient knowledge of the domain. Participants judged the pertinence of experts from diverse disciplines based on the experts' assumed contributions to texts adapted from original articles from Science and Nature. Subjective pertinence judgments were calibrated by comparing them with bibliometrics of the original articles. Furthermore, participants' general science knowledge was controlled. Results showed that participants made well-calibrated pertinence judgments regardless of their level of general science knowledge. Copyright © 2015 Cognitive Science Society, Inc.

An Automated Approach for Ranking Journals to Help in Clinician Decision Support

PubMed Central

Jonnalagadda, Siddhartha R.; Moosavinasab, Soheil; Nath, Chinmoy; Li, Dingcheng; Chute, Christopher G.; Liu, Hongfang

2014-01-01

Point of care access to knowledge from full text journal articles supports decision-making and decreases medical errors. However, it is an overwhelming task to search through full text journal articles and find quality information needed by clinicians. We developed a method to rate journals for a given clinical topic, Congestive Heart Failure (CHF). Our method enables filtering of journals and ranking of journal articles based on source journal in relation to CHF. We also obtained a journal priority score, which automatically rates any journal based on its importance to CHF. Comparing our ranking with data gathered by surveying 169 cardiologists, who publish on CHF, our best Multiple Linear Regression model showed a correlation of 0.880, based on five-fold cross validation. Our ranking system can be extended to other clinical topics. PMID:25954382

Inferring Higher Functional Information for RIKEN Mouse Full-Length cDNA Clones With FACTS

PubMed Central

Nagashima, Takeshi; Silva, Diego G.; Petrovsky, Nikolai; Socha, Luis A.; Suzuki, Harukazu; Saito, Rintaro; Kasukawa, Takeya; Kurochkin, Igor V.; Konagaya, Akihiko; Schönbach, Christian

2003-01-01

FACTS (Functional Association/Annotation of cDNA Clones from Text/Sequence Sources) is a semiautomated knowledge discovery and annotation system that integrates molecular function information derived from sequence analysis results (sequence inferred) with functional information extracted from text. Text-inferred information was extracted from keyword-based retrievals of MEDLINE abstracts and by matching of gene or protein names to OMIM, BIND, and DIP database entries. Using FACTS, we found that 47.5% of the 60,770 RIKEN mouse cDNA FANTOM2 clone annotations were informative for text searches. MEDLINE queries yielded molecular interaction-containing sentences for 23.1% of the clones. When disease MeSH and GO terms were matched with retrieved abstracts, 22.7% of clones were associated with potential diseases, and 32.5% with GO identifiers. A significant number (23.5%) of disease MeSH-associated clones were also found to have a hereditary disease association (OMIM Morbidmap). Inferred neoplastic and nervous system disease represented 49.6% and 36.0% of disease MeSH-associated clones, respectively. A comparison of sequence-based GO assignments with informative text-based GO assignments revealed that for 78.2% of clones, identical GO assignments were provided for that clone by either method, whereas for 21.8% of clones, the assignments differed. In contrast, for OMIM assignments, only 28.5% of clones had identical sequence-based and text-based OMIM assignments. Sequence, sentence, and term-based functional associations are included in the FACTS database (http://facts.gsc.riken.go.jp/), which permits results to be annotated and explored through web-accessible keyword and sequence search interfaces. The FACTS database will be a critical tool for investigating the functional complexity of the mouse transcriptome, cDNA-inferred interactome (molecular interactions), and pathome (pathologies). PMID:12819151

Meaningful main effects or intriguing interactions? Examining the influences of epistemic beliefs and knowledge representations on cognitive processing and conceptual change when learning physics

NASA Astrophysics Data System (ADS)

Franco, Gina M.

The purpose of this study was to investigate the role of epistemic beliefs and knowledge representations in cognitive and metacognitive processing and conceptual change when learning about physics concepts through text. Specifically, I manipulated the representation of physics concepts in texts about Newtonian mechanics and explored how these texts interacted with individuals' epistemic beliefs to facilitate or constrain learning. In accordance with definitions from Royce's (1983) framework of psychological epistemology, texts were developed to present Newtonian concepts in either a rational or a metaphorical format. Seventy-five undergraduate students completed questionnaires designed to measure their epistemic beliefs and their misconceptions about Newton's laws of motion. Participants then read the first of two instructional texts (in either a rational or metaphorical format), and were asked to think aloud while reading. After reading the text, participants completed a recall task and a post-test of selected items regarding Newtonian concepts. These steps were repeated with a second instructional text (in either a rational or metaphorical format, depending on which format was assigned previously). Participants' think-aloud sessions were audio-recorded, transcribed, and then blindly coded, and their recalls were scored for total number of correctly recalled ideas from the text. Changes in misconceptions were analyzed by examining changes in participants' responses to selected questions about Newtonian concepts from pretest to posttest. Results revealed that when individuals' epistemic beliefs were congruent with the knowledge representations in their assigned texts, they performed better on both online measures of learning (e.g., use of processing strategies) and offline products of learning (e.g., text recall, changes in misconceptions) than when their epistemic beliefs were incongruent with the knowledge representations. These results have implications for how researchers conceptualize epistemic beliefs and are in line with contemporary views regarding the context sensitivity of individuals' epistemic beliefs. Moreover, the findings from this study not only support current theory about the dynamic and interactive nature of conceptual change, but also advance empirical work in this area by identifying knowledge representations as a text characteristic that may play an important role in the change process.

Biological knowledge bases using Wikis: combining the flexibility of Wikis with the structure of databases.

PubMed

Brohée, Sylvain; Barriot, Roland; Moreau, Yves

2010-09-01

In recent years, the number of knowledge bases developed using Wiki technology has exploded. Unfortunately, next to their numerous advantages, classical Wikis present a critical limitation: the invaluable knowledge they gather is represented as free text, which hinders their computational exploitation. This is in sharp contrast with the current practice for biological databases where the data is made available in a structured way. Here, we present WikiOpener an extension for the classical MediaWiki engine that augments Wiki pages by allowing on-the-fly querying and formatting resources external to the Wiki. Those resources may provide data extracted from databases or DAS tracks, or even results returned by local or remote bioinformatics analysis tools. This also implies that structured data can be edited via dedicated forms. Hence, this generic resource combines the structure of biological databases with the flexibility of collaborative Wikis. The source code and its documentation are freely available on the MediaWiki website: http://www.mediawiki.org/wiki/Extension:WikiOpener.

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

A Usability Study of a Serious Game in Cognitive Rehabilitation: A Compensatory Navigation Training in Acquired Brain Injury Patients

PubMed Central

van der Kuil, Milan N. A.; Visser-Meily, Johanna M. A.; Evers, Andrea W. M.; van der Ham, Ineke J. M.

2018-01-01

Acquired brain injury patients often report navigation impairments. A cognitive rehabilitation therapy has been designed in the form of a serious game. The aim of the serious game is to aid patients in the development of compensatory navigation strategies by providing exercises in 3D virtual environments on their home computers. The objective of this study was to assess the usability of three critical gaming attributes: movement control in 3D virtual environments, instruction modality and feedback timing. Thirty acquired brain injury patients performed three tasks in which objective measures of usability were obtained. Mouse controlled movement was compared to keyboard controlled movement in a navigation task. Text-based instructions were compared to video-based instructions in a knowledge acquisition task. The effect of feedback timing on performance and motivation was examined in a navigation training game. Subjective usability ratings of all design options were assessed using questionnaires. Results showed that mouse controlled interaction in 3D environments is more effective than keyboard controlled interaction. Patients clearly preferred video-based instructions over text-based instructions, even though video-based instructions were not more effective in context of knowledge acquisition and comprehension. No effect of feedback timing was found on performance and motivation in games designed to train navigation abilities. Overall appreciation of the serious game was positive. The results provide valuable insights in the design choices that facilitate the transfer of skills from serious games to real-life situations. PMID:29922196

A Usability Study of a Serious Game in Cognitive Rehabilitation: A Compensatory Navigation Training in Acquired Brain Injury Patients.

PubMed

van der Kuil, Milan N A; Visser-Meily, Johanna M A; Evers, Andrea W M; van der Ham, Ineke J M

2018-01-01

Acquired brain injury patients often report navigation impairments. A cognitive rehabilitation therapy has been designed in the form of a serious game. The aim of the serious game is to aid patients in the development of compensatory navigation strategies by providing exercises in 3D virtual environments on their home computers. The objective of this study was to assess the usability of three critical gaming attributes: movement control in 3D virtual environments, instruction modality and feedback timing. Thirty acquired brain injury patients performed three tasks in which objective measures of usability were obtained. Mouse controlled movement was compared to keyboard controlled movement in a navigation task. Text-based instructions were compared to video-based instructions in a knowledge acquisition task. The effect of feedback timing on performance and motivation was examined in a navigation training game. Subjective usability ratings of all design options were assessed using questionnaires. Results showed that mouse controlled interaction in 3D environments is more effective than keyboard controlled interaction. Patients clearly preferred video-based instructions over text-based instructions, even though video-based instructions were not more effective in context of knowledge acquisition and comprehension. No effect of feedback timing was found on performance and motivation in games designed to train navigation abilities. Overall appreciation of the serious game was positive. The results provide valuable insights in the design choices that facilitate the transfer of skills from serious games to real-life situations.

COM3/369: Knowledge-based Information Systems: A new approach for the representation and retrieval of medical information

PubMed Central

Mann, G; Birkmann, C; Schmidt, T; Schaeffler, V

1999-01-01

Introduction Present solutions for the representation and retrieval of medical information from online sources are not very satisfying. Either the retrieval process lacks of precision and completeness the representation does not support the update and maintenance of the represented information. Most efforts are currently put into improving the combination of search engines and HTML based documents. However, due to the current shortcomings of methods for natural language understanding there are clear limitations to this approach. Furthermore, this approach does not solve the maintenance problem. At least medical information exceeding a certain complexity seems to afford approaches that rely on structured knowledge representation and corresponding retrieval mechanisms. Methods Knowledge-based information systems are based on the following fundamental ideas. The representation of information is based on ontologies that define the structure of the domain's concepts and their relations. Views on domain models are defined and represented as retrieval schemata. Retrieval schemata can be interpreted as canonical query types focussing on specific aspects of the provided information (e.g. diagnosis or therapy centred views). Based on these retrieval schemata it can be decided which parts of the information in the domain model must be represented explicitly and formalised to support the retrieval process. As representation language propositional logic is used. All other information can be represented in a structured but informal way using text, images etc. Layout schemata are used to assign layout information to retrieved domain concepts. Depending on the target environment HTML or XML can be used. Results Based on this approach two knowledge-based information systems have been developed. The 'Ophthalmologic Knowledge-based Information System for Diabetic Retinopathy' (OKIS-DR) provides information on diagnoses, findings, examinations, guidelines, and reference images related to diabetic retinopathy. OKIS-DR uses combinations of findings to specify the information that must be retrieved. The second system focuses on nutrition related allergies and intolerances. Information on allergies and intolerances of a patient are used to retrieve general information on the specified combination of allergies and intolerances. As a special feature the system generates tables showing food types and products that are tolerated or not tolerated by patients. Evaluation by external experts and user groups showed that the described approach of knowledge-based information systems increases the precision and completeness of knowledge retrieval. Due to the structured and non-redundant representation of information the maintenance and update of the information can be simplified. Both systems are available as WWW based online knowledge bases and CD-ROMs (cf. http://mta.gsf.de topic: products).

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks

PubMed Central

Akimushkin, Camilo; Amancio, Diego Raphael; Oliveira, Osvaldo Novais

2017-01-01

Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network growth mechanisms, but only a few studies have probed the suitability of networks in modeling small chunks of text to grasp stylistic features. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. Since 73% of all series were stationary (ARIMA(p, 0, q)) and the remaining were integrable of first order (ARIMA(p, 1, q)), probability distributions could be obtained for the global network metrics. The metrics exhibit bell-shaped non-Gaussian distributions, and therefore distribution moments were used as learning attributes. With an optimized supervised learning procedure based on a nonlinear transformation performed by Isomap, 71 out of 80 texts were correctly classified using the K-nearest neighbors algorithm, i.e. a remarkable 88.75% author matching success rate was achieved. Hence, purely dynamic fluctuations in network metrics can characterize authorship, thus paving the way for a robust description of large texts in terms of small evolving networks. PMID:28125703

Tag Clouds as a Pathway to Improved Pedagogical Efficacy in Information Systems Courses: A Baseline Study Involving Web 2.0 Technologies

ERIC Educational Resources Information Center

Conn, Samuel S.; English, John; Scheffler, Fred; Hall, Simin

2011-01-01

Various Web 2.0 technologies can be used to support pedagogy. Examples include wikis, blogs, and social media including forum discussions. Online class forum discussions involving electronic text can result in robust strings of data containing meta-knowledge, inherent meaning, themes and patterns. Based on instructional design, learning outcomes…

From Secrets to Science: Technical Writing, Utility, and the Hermetic Tradition in Agricola's "De Re Metallica."

ERIC Educational Resources Information Center

Longo, Bernadette

1997-01-01

Relates that, in the mid-1500s, Agricola combined the traditions of Hermetic secrets and handbooks to compile mining lore into "De Re Metallica," in which he wrote clearly and simply, illustrated information with graphics, and rationalized use of occult knowledge based on utility. States his text paved the way for philosophers to…

Is It Believable When It's Scientific? How Scientific Discourse Style Influences Laypeople's Resolution of Conflicts

ERIC Educational Resources Information Center

Bromme, Rainer; Scharrer, Lisa; Stadtler, Marc; Hömberg, Johanna; Torspecken, Ronja

2015-01-01

Scientific texts are a genre in which adherence to specific discourse conventions allows for conclusions on the scientific integrity of the information and thus on its validity. This study examines whether genre-typical features of scientific discourse influence how laypeople handle conflicting science-based knowledge claims. In two experiments…

Group Work with Adolescents: Principles and Practice. Second Edition. Social Work Practice with Children and Families

ERIC Educational Resources Information Center

Malekoff, Andrew

2006-01-01

This popular text provides essential knowledge and skills for conducting creative, strengths-based group work with adolescents. A rich introduction to the field, enlivened by numerous illustrations from actual sessions, the book provides principles and guidelines for practice in a wide range of settings. The book covers all phases of group work,…

Beyond the Gutenberg Parenthesis: Exploring New Paradigms in Media and Learning

ERIC Educational Resources Information Center

Kenny, Robert F.

2011-01-01

There are those who agree with Tom Pettitt that we are entering into a period where text based literacy is no longer the only measure of intelligence, nor is it the only form of valuable communications and knowledge acquisition for today's media-centric children. As Prensky states, today's youth speak "digital" as their primary language. While his…

Creating exclusive breastfeeding knowledge translation tools with First Nations mothers in Northwest Territories, Canada.

PubMed

Moffitt, Pertice; Dickinson, Raissa

2016-01-01

Background Breastfeeding is an ideal method of infant feeding affecting lifelong health, and yet the uptake of breastfeeding in some Indigenous communities in Canada's north is low. Objective The aims of this project were to determine the rate and determinants of exclusive breastfeeding in a remote community in the Northwest Territories and to create knowledge translation tools to enhance breastfeeding locally. Methods The study methodology followed three steps. Firstly, a series of retrospective chart audits were conducted from hospital birth records of Tł [Formula: see text] chǫ women (n=198) who gave birth during the period of 1 January 2010 to 31 December 2012. A second follow-up chart audit determined the rate of exclusive breastfeeding and was conducted in the local Community Health Centre. Chart audit data included the following factors related to breastfeeding: age of mother, parity, birthweight and Apgar scores. Secondly, semi-structured interviews with a purposive sample of Tł [Formula: see text] chǫ mothers (n=8) and one Elder were conducted to identify breastfeeding practices, beliefs and the most appropriate medium to use to deliver health messages in Tł [Formula: see text] chǫ. Third, based on the information obtained in Step 2, two knowledge translation tools were developed in collaboration with a local community Advisory Committee. Results The rate of exclusive breastfeeding initiation in the Tł [Formula: see text] chǫ region is less than 30%. Physiological and demographic factors related to breastfeeding were identified. Thematic analysis revealed two overarching themes from the data, namely, "the pull to formula" (lifestyle preferences, drug and alcohol use, supplementation practices and limited role models) and "the pull to breast feeding" (traditional feeding method, spiritual practice and increased bonding with infant). Conclusion There are a myriad of influences on breastfeeding for women living in remote locations. Ultimately, society informs the choice of infant feeding for the new mother, since mothers' feeding choices are based on contextual realities and circumstances in their lives that are out of their control. As health care providers, it is imperative that we recognize the realities of women's lives and the overlapping social determinants of health that may limit a mother's ability or choice to breastfeed. Further health promotion efforts, grounded in community-based research and a social determinants framework, are needed to improve prenatal and postnatal care of Indigenous women and children in Canada.

Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models.

PubMed

An, Gary

2009-01-01

The sheer volume of biomedical research threatens to overwhelm the capacity of individuals to effectively process this information. Adding to this challenge is the multiscale nature of both biological systems and the research community as a whole. Given this volume and rate of generation of biomedical information, the research community must develop methods for robust representation of knowledge in order for individuals, and the community as a whole, to "know what they know." Despite increasing emphasis on "data-driven" research, the fact remains that researchers guide their research using intuitively constructed conceptual models derived from knowledge extracted from publications, knowledge that is generally qualitatively expressed using natural language. Agent-based modeling (ABM) is a computational modeling method that is suited to translating the knowledge expressed in biomedical texts into dynamic representations of the conceptual models generated by researchers. The hierarchical object-class orientation of ABM maps well to biomedical ontological structures, facilitating the translation of ontologies into instantiated models. Furthermore, ABM is suited to producing the nonintuitive behaviors that often "break" conceptual models. Verification in this context is focused at determining the plausibility of a particular conceptual model, and qualitative knowledge representation is often sufficient for this goal. Thus, utilized in this fashion, ABM can provide a powerful adjunct to other computational methods within the research process, as well as providing a metamodeling framework to enhance the evolution of biomedical ontologies.

Knowledge Retrieval Solutions.

ERIC Educational Resources Information Center

Khan, Kamran

1998-01-01

Excalibur RetrievalWare offers true knowledge retrieval solutions. Its fundamental technologies, Adaptive Pattern Recognition Processing and Semantic Networks, have capabilities for knowledge discovery and knowledge management of full-text, structured and visual information. The software delivers a combination of accuracy, extensibility,…

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Mapping annotations with textual evidence using an scLDA model.

PubMed

Jin, Bo; Chen, Vicky; Chen, Lujia; Lu, Xinghua

2011-01-01

Most of the knowledge regarding genes and proteins is stored in biomedical literature as free text. Extracting information from complex biomedical texts demands techniques capable of inferring biological concepts from local text regions and mapping them to controlled vocabularies. To this end, we present a sentence-based correspondence latent Dirichlet allocation (scLDA) model which, when trained with a corpus of PubMed documents with known GO annotations, performs the following tasks: 1) learning major biological concepts from the corpus, 2) inferring the biological concepts existing within text regions (sentences), and 3) identifying the text regions in a document that provides evidence for the observed annotations. When applied to new gene-related documents, a trained scLDA model is capable of predicting GO annotations and identifying text regions as textual evidence supporting the predicted annotations. This study uses GO annotation data as a testbed; the approach can be generalized to other annotated data, such as MeSH and MEDLINE documents.

Mentor Texts and Funds of Knowledge: Situating Writing within Our Students' Worlds

ERIC Educational Resources Information Center

Newman, Beatrice Mendez

2012-01-01

The funds of knowledge concept serves as scaffolding for encouraging students to draw on background experiences and home language to generate authentic writing. This article describes and illustrates several classroom strategies, including 1) using culturally relevant mentor texts; 2) applying Nancie Atwell's writing territories concept; 3)…

An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

Knowledge Activation, Integration, and Validation during Narrative Text Comprehension

ERIC Educational Resources Information Center

Cook, Anne E.; O'Brien, Edward J.

2014-01-01

Previous text comprehension studies using the contradiction paradigm primarily tested assumptions of the activation mechanism involved in reading. However, the nature of the contradiction in such studies relied on validation of information in readers' general world knowledge. We directly tested this validation process by varying the strength of…

Using Literature Circles to Enhance Student Knowledge of Nonfiction Text

ERIC Educational Resources Information Center

Whitworth, Amanda

2017-01-01

This mixed methods action research study explored how students reacted to using literature circles to enhance their knowledge and understanding of reading nonfiction text as compared to students using guided reading. This study showed a minimal improvement for students participating in the literature circle group in overall understanding of…

PASBio: predicate-argument structures for event extraction in molecular biology

PubMed Central

Wattarujeekrit, Tuangthong; Shah, Parantu K; Collier, Nigel

2004-01-01

Background The exploitation of information extraction (IE), a technology aiming to provide instances of structured representations from free-form text, has been rapidly growing within the molecular biology (MB) research community to keep track of the latest results reported in literature. IE systems have traditionally used shallow syntactic patterns for matching facts in sentences but such approaches appear inadequate to achieve high accuracy in MB event extraction due to complex sentence structure. A consensus in the IE community is emerging on the necessity for exploiting deeper knowledge structures such as through the relations between a verb and its arguments shown by predicate-argument structure (PAS). PAS is of interest as structures typically correspond to events of interest and their participating entities. For this to be realized within IE a key knowledge component is the definition of PAS frames. PAS frames for non-technical domains such as newswire are already being constructed in several projects such as PropBank, VerbNet, and FrameNet. Knowledge from PAS should enable more accurate applications in several areas where sentence understanding is required like machine translation and text summarization. In this article, we explore the need to adapt PAS for the MB domain and specify PAS frames to support IE, as well as outlining the major issues that require consideration in their construction. Results We introduce PASBio by extending a model based on PropBank to the MB domain. The hypothesis we explore is that PAS holds the key for understanding relationships describing the roles of genes and gene products in mediating their biological functions. We chose predicates describing gene expression, molecular interactions and signal transduction events with the aim of covering a number of research areas in MB. Analysis was performed on sentences containing a set of verbal predicates from MEDLINE and full text journals. Results confirm the necessity to analyze PAS specifically for MB domain. Conclusions At present PASBio contains the analyzed PAS of over 30 verbs, publicly available on the Internet for use in advanced applications. In the future we aim to expand the knowledge base to cover more verbs and the nominal form of each predicate. PMID:15494078

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

PubMed Central

Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming

2012-01-01

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823

Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts

PubMed Central

Zhang, Shaodian; Elhadad, Nóemie

2013-01-01

Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work. PMID:23954592

Neural network for interpretation of multi-meaning Chinese words

NASA Astrophysics Data System (ADS)

He, Qianhua; Xu, Bingzheng

1994-03-01

We proposed a neural network that can interpret multi-meaning Chinese words correctly by using context information. The self-organized network, designed for translating Chinese to English, builds a context according to key words of the processed text and utilizes it to interpret multi-meaning words correctly. The network is generated automatically basing on a Chinese-English dictionary and a knowledge-base of weights, and can adapt to the change of contexts. Simulation experiments have proved that the network worked as expected.

Reading Time as Evidence for Mental Models in Understanding Physics

NASA Astrophysics Data System (ADS)

Brookes, David T.; Mestre, José; Stine-Morrow, Elizabeth A. L.

2007-11-01

We present results of a reading study that show the usefulness of probing physics students' cognitive processing by measuring reading time. According to contemporary discourse theory, when people read a text, a network of associated inferences is activated to create a mental model. If the reader encounters an idea in the text that conflicts with existing knowledge, the construction of a coherent mental model is disrupted and reading times are prolonged, as measured using a simple self-paced reading paradigm. We used this effect to study how "non-Newtonian" and "Newtonian" students create mental models of conceptual systems in physics as they read texts related to the ideas of Newton's third law, energy, and momentum. We found significant effects of prior knowledge state on patterns of reading time, suggesting that students attempt to actively integrate physics texts with their existing knowledge.

Palliative care clinicians' knowledge of the law regarding the use of the Deprivation of Liberty Safeguards (DoLS).

PubMed

Barry, Caroline; Spathis, Anna; Treaddell, Sarah; Carding, Sally; Barclay, Stephen

2017-04-24

To examine palliative care clinicians' level of knowledge of the law regarding the use of the Deprivation of Liberty Safeguards (DoLS). Regional postal survey of palliative care clinicians working in hospices in the East of England, undertaken in April 2015. Clinicians' level of knowledge was assessed by their response to 7 factual questions. Data regarding self-reported levels of confidence in applying the Safeguards was collected, alongside information regarding the number of times they had used DoLS in practice. A free-text section invited additional comments from participants. There were 47 responses from 14 different organisations; a response rate of 68%. Respondents included consultants, specialty and associate specialists, registrars, nurses and social workers. Higher self-reported confidence and training in the use of DoLS was associated with higher factual knowledge. Consultants had the highest level of knowledge, training and experience. Doctors of other grades, nurses and social workers recorded less knowledge and experience and scored lower in the knowledge sections. The free-text comments revealed difficulty applying the Safeguards in practice, particularly among the consultant responses, based around several themes: insufficient guidance on how to use the Safeguards, process after death, uncertainty as to relevance to palliative care and delays in assessments. Clinicians working in palliative care have good levels of knowledge of the DoLS. Despite this concerns were raised, particularly by consultants; uncertainty as to when they should be used and the relevance of the Safeguards in clinical practice. Further guidance should be given to clinicians working in this specialty to ensure that clinical practice is both lawful and in the patients' best interests. 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/.

Methodology to build medical ontology from textual resources.

PubMed

Baneyx, Audrey; Charlet, Jean; Jaulent, Marie-Christine

2006-01-01

In the medical field, it is now established that the maintenance of unambiguous thesauri goes through ontologies. Our research task is to help pneumologists code acts and diagnoses with a software that represents medical knowledge through a domain ontology. In this paper, we describe our general methodology aimed at knowledge engineers in order to build various types of medical ontologies based on terminology extraction from texts. The hypothesis is to apply natural language processing tools to textual patient discharge summaries to develop the resources needed to build an ontology in pneumology. Results indicate that the joint use of distributional analysis and lexico-syntactic patterns performed satisfactorily for building such ontologies.

FERN Ethnomedicinal Plant Database: Exploring Fern Ethnomedicinal Plants Knowledge for Computational Drug Discovery.

PubMed

Thakar, Sambhaji B; Ghorpade, Pradnya N; Kale, Manisha V; Sonawane, Kailas D

2015-01-01

Fern plants are known for their ethnomedicinal applications. Huge amount of fern medicinal plants information is scattered in the form of text. Hence, database development would be an appropriate endeavor to cope with the situation. So by looking at the importance of medicinally useful fern plants, we developed a web based database which contains information about several group of ferns, their medicinal uses, chemical constituents as well as protein/enzyme sequences isolated from different fern plants. Fern ethnomedicinal plant database is an all-embracing, content management web-based database system, used to retrieve collection of factual knowledge related to the ethnomedicinal fern species. Most of the protein/enzyme sequences have been extracted from NCBI Protein sequence database. The fern species, family name, identification, taxonomy ID from NCBI, geographical occurrence, trial for, plant parts used, ethnomedicinal importance, morphological characteristics, collected from various scientific literatures and journals available in the text form. NCBI's BLAST, InterPro, phylogeny, Clustal W web source has also been provided for the future comparative studies. So users can get information related to fern plants and their medicinal applications at one place. This Fern ethnomedicinal plant database includes information of 100 fern medicinal species. This web based database would be an advantageous to derive information specifically for computational drug discovery, botanists or botanical interested persons, pharmacologists, researchers, biochemists, plant biotechnologists, ayurvedic practitioners, doctors/pharmacists, traditional medicinal users, farmers, agricultural students and teachers from universities as well as colleges and finally fern plant lovers. This effort would be useful to provide essential knowledge for the users about the adventitious applications for drug discovery, applications, conservation of fern species around the world and finally to create social awareness.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

The Effects of Study Tasks in a Computer-Based Chemistry Learning Environment

NASA Astrophysics Data System (ADS)

Urhahne, Detlef; Nick, Sabine; Poepping, Anna Christin; Schulz, Sarah Jayne

2013-12-01

The present study examines the effects of different study tasks on the acquisition of knowledge about acids and bases in a computer-based learning environment. Three different task formats were selected to create three treatment conditions: learning with gap-fill and matching tasks, learning with multiple-choice tasks, and learning only from text and figures without any additional tasks. Participants were 196 ninth-grade students who learned with a self-developed multimedia program in a pretest-posttest control group design. Research results reveal that gap-fill and matching tasks were most effective in promoting knowledge acquisition, followed by multiple-choice tasks, and no tasks at all. The findings are in line with previous research on this topic. The effects can possibly be explained by the generation-recognition model, which predicts that gap-fill and matching tasks trigger more encompassing learning processes than multiple-choice tasks. It is concluded that instructional designers should incorporate more challenging study tasks for enhancing the effectiveness of computer-based learning environments.

Relating UMLS semantic types and task-based ontology to computer-interpretable clinical practice guidelines.

PubMed

Kumar, Anand; Ciccarese, Paolo; Quaglini, Silvana; Stefanelli, Mario; Caffi, Ezio; Boiocchi, Lorenzo

2003-01-01

Medical knowledge in clinical practice guideline (GL) texts is the source of task-based computer-interpretable clinical guideline models (CIGMs). We have used Unified Medical Language System (UMLS) semantic types (STs) to understand the percentage of GL text which belongs to a particular ST. We also use UMLS semantic network together with the CIGM-specific ontology to derive a semantic meaning behind the GL text. In order to achieve this objective, we took nine GL texts from the National Guideline Clearinghouse (NGC) and marked up the text dealing with a particular ST. The STs we took into consideration were restricted taking into account the requirements of a task-based CIGM. We used DARPA Agent Markup Language and Ontology Inference Layer (DAML + OIL) to create the UMLS and CIGM specific semantic network. For the latter, as a bench test, we used the 1999 WHO-International Society of Hypertension Guidelines for the Management of Hypertension. We took into consideration the UMLS STs closest to the clinical tasks. The percentage of the GL text dealing with the ST "Health Care Activity" and subtypes "Laboratory Procedure", "Diagnostic Procedure" and "Therapeutic or Preventive Procedure" were measured. The parts of text belonging to other STs or comments were separated. A mapping of terms belonging to other STs was done to the STs under "HCA" for representation in DAML + OIL. As a result, we found that the three STs under "HCA" were the predominant STs present in the GL text. In cases where the terms of related STs existed, they were mapped into one of the three STs. The DAML + OIL representation was able to describe the hierarchy in task-based CIGMs. To conclude, we understood that the three STs could be used to represent the semantic network of the task-bases CIGMs. We identified some mapping operators which could be used for the mapping of other STs into these.

A Multidimensional Investigation of Deep-Level and Surface-Level Processing

ERIC Educational Resources Information Center

Dinsmore, Daniel L.; Alexander, Patricia A.

2016-01-01

This study examines the moderating effects of a situational factor (i.e., text type) and an individual factor (i.e., subject-matter knowledge) on the relation between depth of processing and performance. One-hundred and fifty-one undergraduates completed measures of subject-matter knowledge, read either an expository or persuasive text about the…

Reading Processing Skills among EFL Learners in Different Proficiency Levels

ERIC Educational Resources Information Center

Dhanapala, Kusumi Vasantha; Yamada, Jun

2015-01-01

This study aims to understand how EFL learners in different reading proficiency levels comprehend L2 texts, using five-component skills involving measures of (1) vocabulary knowledge, (2) drawing inferences and predictions, (3) knowledge of text structure and discourse organization, (4) identifying the main idea and summarizing skills, and (5)…

Curriculum Knowledge and Justice: Content, Competency and Concept

ERIC Educational Resources Information Center

Winter, Christine

2011-01-01

This study is interested in understanding the configurations of knowledge underpinning three examples of curriculum policy texts in the specific case of the school subject of geography. The three policy texts are the 1991 Geography National Curriculum (GNC), the "Opening minds" curriculum and the GNC 2007. I start with the proposal that…

Generating New HRD Knowledge: Discourse, Gender, and Theory Building

ERIC Educational Resources Information Center

Storberg-Walker, Julia; Bierema, Laura

2007-01-01

For this manuscript, a classic management text was deconstructed using postmodern methods. The purpose was twofold: to gain an understanding of how this text connected knowledge and gender; and to provide readers with a sample of deconstruction. The value of this type of analysis for HRD will be made clear. Unsurprisingly, because the manuscript…

Automated Extraction of Substance Use Information from Clinical Texts.

PubMed

Wang, Yan; Chen, Elizabeth S; Pakhomov, Serguei; Arsoniadis, Elliot; Carter, Elizabeth W; Lindemann, Elizabeth; Sarkar, Indra Neil; Melton, Genevieve B

2015-01-01

Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g., temporal and status) based on Stanford Typed Dependencies. The developed NLP system leveraged linguistic resources and domain knowledge from a multi-site social history study, Propbank and the MiPACQ corpus. The system attained F-scores of 89.8, 84.6 and 89.4 respectively for alcohol, drug, and nicotine use statement detection, as well as average F-scores of 82.1, 90.3, 80.8, 88.7, 96.6, and 74.5 respectively for extraction of attributes. Our results suggest that NLP systems can achieve good performance when augmented with linguistic resources and domain knowledge when applied to a wide breadth of substance use free text clinical notes.

An improved association-mining research for exploring Chinese herbal property theory: based on data of the Shennong's Classic of Materia Medica.

PubMed

Jin, Rui; Lin, Zhi-jian; Xue, Chun-miao; Zhang, Bing

2013-09-01

Knowledge Discovery in Databases is gaining attention and raising new hopes for traditional Chinese medicine (TCM) researchers. It is a useful tool in understanding and deciphering TCM theories. Aiming for a better understanding of Chinese herbal property theory (CHPT), this paper performed an improved association rule learning to analyze semistructured text in the book entitled Shennong's Classic of Materia Medica. The text was firstly annotated and transformed to well-structured multidimensional data. Subsequently, an Apriori algorithm was employed for producing association rules after the sensitivity analysis of parameters. From the confirmed 120 resulting rules that described the intrinsic relationships between herbal property (qi, flavor and their combinations) and herbal efficacy, two novel fundamental principles underlying CHPT were acquired and further elucidated: (1) the many-to-one mapping of herbal efficacy to herbal property; (2) the nonrandom overlap between the related efficacy of qi and flavor. This work provided an innovative knowledge about CHPT, which would be helpful for its modern research.

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406

Documenting research with transgender and gender diverse people: protocol for an evidence map and thematic analysis.

PubMed

Marshall, Zack; Welch, Vivian; Thomas, James; Brunger, Fern; Swab, Michelle; Shemilt, Ian; Kaposy, Chris

2017-02-20

There is limited information about how transgender, gender diverse, and Two-Spirit (trans) people have been represented and studied by researchers. The objectives of this study are to (1) map and describe trans research in the social sciences, sciences, humanities, health, education, and business, (2) identify evidence gaps and opportunities for more responsible research with trans people, (3) assess the use of text mining for study identification, and (4) increase access to trans research for key stakeholders through the creation of a web-based evidence map. Study design was informed by community consultations and pilot searches. Eligibility criteria were established to include all original research of any design, including trans people or their health information, and published in English in peer-reviewed journals. A complex electronic search strategy based on relevant concepts in 15 databases was developed to obtain a broad range of results linked to transgender, gender diverse, and Two-Spirit individuals and communities. Searches conducted in early 2015 resulted in 25,242 references after removal of duplicates. Based on the number of references, resources, and an objective to capture upwards of 90% of the existing literature, this study is a good candidate for text mining using Latent Dirichlet Allocation to improve efficiency of the screening process. The following information will be collected for evidence mapping: study topic, study design, methods and data sources, recruitment strategies, sample size, sample demographics, researcher name and affiliation, country where research was conducted, funding source, and year of publication. The proposed research incorporates an extensive search strategy, text mining, and evidence map; it therefore has the potential to build on knowledge in several fields. Review results will increase awareness of existing trans research, identify evidence gaps, and inform strategic research prioritization. Publishing the map online will improve access to research for key stakeholders including community members, policy makers, and healthcare providers. This study will also contribute to knowledge in the area of text mining for study identification by providing an example of how semi-automation performs for screening on title and abstract and on full text.

Memory and Comprehension for Health Information among Older Adults: Distinguishing the Effects of Domain-General and Domain-Specific Knowledge

PubMed Central

Chin, Jessie; Payne, Brennan; Gao, Xuefei; Conner-Garcia, Thembi; Graumlich, James F.; Murray, Michael D.; Morrow, Daniel G.; Stine-Morrow, Elizabeth A.L.

2014-01-01

While there is evidence that knowledge influences understanding of health information, less is known about the processing mechanisms underlying this effect and its impact on memory. We used the moving window paradigm to examine how older adults varying in domain-general crystallized ability (verbal ability) and health knowledge allocate attention to understand health and domain-general texts. Participants (n=107, aged 60 to 88 yrs) read and recalled single sentences about hypertension and about non-health topics. Mixed-effects modeling of word-by-word reading times suggested that domain-general crystallized ability increased conceptual integration regardless of text domain, while health knowledge selectively increased resource allocation to conceptual integration at clause boundaries in health texts. These patterns of attentional allocation were related to subsequent recall performance. Although older adults with lower levels of crystallized ability were less likely to engage in integrative processing, when they did, this strategy had a compensatory effect in improving recall. These findings suggest that semantic integration during reading is an important comprehension process that supports the construction of the memory representation and is engendered by knowledge. Implications of the findings for theories of text processing and memory as well as for designing patient education materials are discussed. PMID:24787361

Memory and comprehension for health information among older adults: distinguishing the effects of domain-general and domain-specific knowledge.

PubMed

Chin, Jessie; Payne, Brennan; Gao, Xuefei; Conner-Garcia, Thembi; Graumlich, James F; Murray, Michael D; Morrow, Daniel G; Stine-Morrow, Elizabeth A L

2015-01-01

While there is evidence that knowledge influences understanding of health information, less is known about the processing mechanisms underlying this effect and its impact on memory. We used the moving window paradigm to examine how older adults varying in domain-general crystallised ability (verbal ability) and health knowledge allocate attention to understand health and domain-general texts. Participants (n = 107, age: 60-88 years) read and recalled single sentences about hypertension and about non-health topics. Mixed-effects modelling of word-by-word reading times suggested that domain-general crystallised ability increased conceptual integration regardless of text domain, while health knowledge selectively increased resource allocation to conceptual integration at clause boundaries in health texts. These patterns of attentional allocation were related to subsequent recall performance. Although older adults with lower levels of crystallised ability were less likely to engage in integrative processing, when they did, this strategy had a compensatory effect in improving recall. These findings suggest that semantic integration during reading is an important comprehension process that supports the construction of the memory representation and is engendered by knowledge. Implications of the findings for theories of text processing and memory as well as for designing patient education materials are discussed.

Constructing Model of Relationship among Behaviors and Injuries to Products Based on Large Scale Text Data on Injuries

NASA Astrophysics Data System (ADS)

Nomori, Koji; Kitamura, Koji; Motomura, Yoichi; Nishida, Yoshifumi; Yamanaka, Tatsuhiro; Komatsubara, Akinori

In Japan, childhood injury prevention is urgent issue. Safety measures through creating knowledge of injury data are essential for preventing childhood injuries. Especially the injury prevention approach by product modification is very important. The risk assessment is one of the most fundamental methods to design safety products. The conventional risk assessment has been carried out subjectively because product makers have poor data on injuries. This paper deals with evidence-based risk assessment, in which artificial intelligence technologies are strongly needed. This paper describes a new method of foreseeing usage of products, which is the first step of the evidence-based risk assessment, and presents a retrieval system of injury data. The system enables a product designer to foresee how children use a product and which types of injuries occur due to the product in daily environment. The developed system consists of large scale injury data, text mining technology and probabilistic modeling technology. Large scale text data on childhood injuries was collected from medical institutions by an injury surveillance system. Types of behaviors to a product were derived from the injury text data using text mining technology. The relationship among products, types of behaviors, types of injuries and characteristics of children was modeled by Bayesian Network. The fundamental functions of the developed system and examples of new findings obtained by the system are reported in this paper.

Network of anatomical texts (NAnaTex), an open-source project for visualizing the interaction between anatomical terms.

PubMed

Momota, Ryusuke; Ohtsuka, Aiji

2018-01-01

Anatomy is the science and art of understanding the structure of the body and its components in relation to the functions of the whole-body system. Medicine is based on a deep understanding of anatomy, but quite a few introductory-level learners are overwhelmed by the sheer amount of anatomical terminology that must be understood, so they regard anatomy as a dull and dense subject. To help them learn anatomical terms in a more contextual way, we started a new open-source project, the Network of Anatomical Texts (NAnaTex), which visualizes relationships of body components by integrating text-based anatomical information using Cytoscape, a network visualization software platform. Here, we present a network of bones and muscles produced from literature descriptions. As this network is primarily text-based and does not require any programming knowledge, it is easy to implement new functions or provide extra information by making changes to the original text files. To facilitate collaborations, we deposited the source code files for the network into the GitHub repository ( https://github.com/ryusukemomota/nanatex ) so that anybody can participate in the evolution of the network and use it for their own non-profit purposes. This project should help not only introductory-level learners but also professional medical practitioners, who could use it as a quick reference.

A Comparative Analysis of the Impact of Cooperative versus Textbook-Based Individual Prereading Activities on the Reading Comprehension of Students of Spanish

ERIC Educational Resources Information Center

Osa-Melero, Lucia

2012-01-01

Reading texts with historical and sociopolitical content in a foreign language is often a challenge for second language students. The obstacles encountered by students should be of concern to language instructors. Lack of background knowledge frequently causes the reader to abandon the reading activity with a sense of disappointment and…

Planning Multisentential English Text Using Communicative Acts

DTIC Science & Technology

1990-12-01

bowl. Add the flour and the baking soda. Stir in the chocolate pieces. Grease a cookie sheet. Spoon the cookie dough onto the cookie sheet. Bake it at...Chocolate Chip Cookies ......................................................................................... 185 Figure 6.5 identify-location Plan...34), and recurses on subactions as required. The plan operators of Figures 6.1 and 6.3 were tested with a small knowledge base of cookie recipe

Tutoring Mathematical Word Problems Using Solution Trees: Text Comprehension, Situation Comprehension, and Mathematization in Solving Story Problems. Research Report No. 8.

ERIC Educational Resources Information Center

Reusser, Kurt; And Others

The main concern of this paper is on the psychological processes of how students understand and solve mathematical word problems, and on how this knowledge can be applied to computer-based tutoring. It is argued that only a better understanding of the psychological requirements for understanding and solving those problems will lead to…

Using comprehension strategies with authentic text in a college chemistry course

NASA Astrophysics Data System (ADS)

Cain, Stephen Daniel

College science students learn important topics by reading textbooks, which contain dense technical prose. Comprehension strategies are known to increase learning from reading. One class of comprehension strategies, called elaboration strategies, is intended to link new information with prior knowledge. Elaboration strategies have an appeal in science courses where new information frequently depends on previously learned information. The purpose of this study was to determine the effectiveness of an elaboration strategy in an authentic college environment. General chemistry students read text about Lewis structures, figures drawn by chemists to depict molecules, while assigned to use either an elaboration strategy, namely elaborative interrogation, or another strategy, rereading, which served as a placebo control. Two texts of equal length were employed in this pretest-posttest experimental design. One was composed by the researcher. The other was an excerpt from a college textbook and contained a procedure for constructing Lewis structures. Students (N = 252) attending a large community college were randomly assigned to one of the two texts and assigned one of the two strategies. The elaborative interrogation strategy was implemented with instructions to answer why-questions posed throughout the reading. Answering why-questions has been hypothesized to activate prior knowledge of a topic, and thus to aid in cognitively connecting new material with prior knowledge. The rereading strategy was implemented with instructions to read text twice. The use of authentic text was one of only a few instances of applying elaborative interrogation with a textbook. In addition, previous studies have generally focused on the learning of facts contained in prose. The application of elaborative interrogation to procedural text has not been previously reported. Results indicated that the more effective strategy was undetermined when reading authentic text in this setting. However, prior knowledge level was identified as a statistically significant factor for learning from authentic text. That is, students with high prior knowledge learned more, regardless of assigned strategy. Another descriptive study was conducted with a separate student sample (N = 34). Previously reported Lewis structure research was replicated. The trend of difficulty for 50 structures in the earlier work was supported.

Addressing the knowledge gap: sexual violence and harassment in the UK Armed Forces.

PubMed

Godier, Lauren R; Fossey, M

2017-09-06

Despite media interest in alleged sexual violence and harassment in the UK military, there remains a paucity of UK-based peer-reviewed research in this area. Ministry of Defence and service-specific reports support the suggestion that UK service personnel may be at risk of experiencing sexual harassment. These reports however highlight a reluctance by service personnel to report sexual harassment through official channels. In this article, we discuss the paucity of UK-based research pertaining to the prevalence and impact of sexual harassment in the military, explore potential reasons for this gap in knowledge and outline future directions and priorities for academic research. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Semantic relatedness for evaluation of course equivalencies

NASA Astrophysics Data System (ADS)

Yang, Beibei

Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the Brown corpus, or more recently, Wikipedia. This dissertation proposes two approaches to applying semantic relatedness to the problem of suggesting transfer course equivalencies. Two course descriptions are given as input to feed the proposed algorithms, which output a value that can be used to help determine if the courses are equivalent. The first proposed approach uses traditional knowledge sources such as WordNet and corpora for courses from multiple fields of study. The second approach uses Wikipedia, the openly-editable encyclopedia, and it focuses on courses from a technical field such as Computer Science. This work shows that it is promising to adapt semantic relatedness to the education field for matching equivalencies between transfer courses. A semantic relatedness measure using traditional knowledge sources such as WordNet performs relatively well on non-technical courses. However, due to the "knowledge acquisition bottleneck," such a resource is not ideal for technical courses, which use an extensive and growing set of technical terms. To address the problem, this work proposes a Wikipedia-based approach which is later shown to be more correlated to human judgment compared to previous work.

Combining rules, background knowledge and change patterns to maintain semantic annotations.

PubMed

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Combining rules, background knowledge and change patterns to maintain semantic annotations

PubMed Central

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system. PMID:29854115

Effects of cardiorespiratory fitness and weight status on knowledge of physical activity and fitness, attitude toward physical education, and physical activity.

PubMed

Chen, Senlin; Gu, Xiangli

2018-02-20

The purpose of this study was to examine the effects of cardiorespiratory fitness and weight status on knowledge of physical activity and fitness (PAF knowledge), attitude toward physical education (PE), and physical activity. A total of 343 middle school students participated in the study (Age: M/SD = 12.76/.94, ranging from 11 to 14 years old). PE Metrics™ was used to measure PAF knowledge, and Attitude toward Physical Education Questionnaire and Youth Activity Profile were used to measure attitude, physical activity and sedentary behavior. Fitness and weight status were assessed using FitnessGram and converted to in Healthy Fitness Zone (HFZ) or Not in HFZ. Two-way multivariate analyses of covariance (MANCOVA; gender and grade as covariates) showed a significant group effect for cardiorespiratory fitness (Λ Pilla  = .07, F 4,255  = 5.03, p = .001, [Formula: see text] = .07) but not for weight status (p = .57). PAF knowledge (F 1,258  = 9.49, p < .01, [Formula: see text]= .04), attitude (F 1,258  = 4.45, p < .05, [Formula: see text]= .02) and sedentary behavior (F 1,258  = 6.89, p < .01, [Formula: see text]= .03) all favored the HFZ group. The findings reinforce the importance of promoting cardiorespiratory fitness in middle school PE as students acquire attitude, knowledge, and behaviors needed for active-living.

Text Mining of UU-ITE Implementation in Indonesia

NASA Astrophysics Data System (ADS)

Hakim, Lukmanul; Kusumasari, Tien F.; Lubis, Muharman

2018-04-01

At present, social media and networks act as one of the main platforms for sharing information, idea, thought and opinions. Many people share their knowledge and express their views on the specific topics or current hot issues that interest them. The social media texts have rich information about the complaints, comments, recommendation and suggestion as the automatic reaction or respond to government initiative or policy in order to overcome certain issues.This study examines the sentiment from netizensas part of citizen who has vocal sound about the implementation of UU ITE as the first cyberlaw in Indonesia as a means to identify the current tendency of citizen perception. To perform text mining techniques, this study used Twitter Rest API while R programming was utilized for the purpose of classification analysis based on hierarchical cluster.

Intelligent Text Retrieval and Knowledge Acquisition from Texts for NASA Applications: Preprocessing Issues

NASA Technical Reports Server (NTRS)

2001-01-01

In this contract, which is a component of a larger contract that we plan to submit in the coming months, we plan to study the preprocessing issues which arise in applying natural language processing techniques to NASA-KSC problem reports. The goals of this work will be to deal with the issues of: a) automatically obtaining the problem reports from NASA-KSC data bases, b) the format of these reports and c) the conversion of these reports to a format that will be adequate for our natural language software. At the end of this contract, we expect that these problems will be solved and that we will be ready to apply our natural language software to a text database of over 1000 KSC problem reports.

Soils: An Introduction, Fifth Edition

NASA Astrophysics Data System (ADS)

Baisden, W. Troy

Understanding the links among global biogeochemical cycles, ecology, hydrology and climate demands a knowledge base that has traditionally been considered soil science. However, for soil science to play a role in this understanding, geologists, hydrologists, ecologists, climatologists, and many others must have a fundamental understanding of soil science. Do introductory soil science texts speak to this audience?To address this question, I reviewed the fifth edition of a textbook that set out in its original edition to accomplish just this goal—to be the introductory soil science text for students outside the discipline of soil science. As such, Singer and Munns' Soils:An Introduction must be compared to The Nature and Properties of Soils by N.C. Brady and R.R. Weil, a standard text directly descended from a first edition published in 1922.

Students' Metacomprehension Knowledge: Components That Predict Comprehension Performance

ERIC Educational Resources Information Center

Zabrucky, Karen M.; Moore, DeWayne; Agler, Lin-Miao Lin; Cummings, Andrea M.

2015-01-01

In the present study, we assessed students' metacomprehension knowledge and examined the components of knowledge most related to comprehension of expository texts. We used the Revised Metacomprehension Scale (RMCS) to investigate the relations between students' metacomprehension knowledge and comprehension performance. Students who evaluated and…

Overcoming the Inert Knowledge Problem in Learning from Expository Text.

ERIC Educational Resources Information Center

Cote, Nathalie

Students often fail to store new information in memory in a way that is accessible or useful. The information they have acquired is inert. This paper examines the inert knowledge problem in the context of learning from informational expository text. Kintsch and van Dijk (1978) have suggested a framework for understanding learning from expository…

Empirical analysis of knowledge bases to support structured output in the Arden syntax.

PubMed

Jenders, Robert A

2013-01-01

Structured output has been suggested for the Arden Syntax to facilitate interoperability. Tabulate the components of WRITE statements in a corpus of medical logic modules (MLMs)in order to validate requiring structured output. WRITE statements were tabulated in 258 MLMs from 2 organizations. In a total of 351 WRITE statements, email destinations (226) predominated, and 39 orders and 40 coded output elements also were tabulated. Free-text strings predominated as the message data. Arden WRITE statements contain considerable potentially structured data now included as free text. A future, normative structured WRITE statement must address a variety of data types and destinations.

Individual Differences in Accurately Judging Personality From Text.

PubMed

Hall, Judith A; Goh, Jin X; Mast, Marianne Schmid; Hagedorn, Christian

2016-08-01

This research examines correlates of accuracy in judging Big Five traits from first-person text excerpts. Participants in six studies were recruited from psychology courses or online. In each study, participants performed a task of judging personality from text and performed other ability tasks and/or filled out questionnaires. Participants who were more accurate in judging personality from text were more likely to be female; had personalities that were more agreeable, conscientious, and feminine, and less neurotic and dominant (all controlling for participant gender); scored higher on empathic concern; self-reported more interest in, and attentiveness to, people's personalities in their daily lives; and reported reading more for pleasure, especially fiction. Accuracy was not associated with SAT scores but had a significant relation to vocabulary knowledge. Accuracy did not correlate with tests of judging personality and emotion based on audiovisual cues. This research is the first to address individual differences in accurate judgment of personality from text, thus adding to the literature on correlates of the good judge of personality. © 2015 Wiley Periodicals, Inc.

Feminism, biomedicine and the 'reproductive destiny' of women in clinical texts on the birth control pill.

PubMed

Carson, Andrea

2018-07-01

The birth control pill is one of the most popular forms of contraception in North America and has been a key player in women's rights activism for over 50 years. In this paper, I conduct a feminist deconstructive analysis of 12 biomedical texts on the birth control pill, published between 1965 and 2016. This study is situated amongst the feminist scholarship that challenges the representation of women's bodies in biomedicine. Findings suggest that clinical texts on the birth control pill continue to universalise women's lives and experiences, and essentialise them based on their reproductive capacities. One way the texts accomplish this is by making women absent or passive in the literature thereby losing concern for the diversity of their lives, interpretations and identities as more than reproductive beings. The consequence of such representations is that biomedical texts disseminate limited forms of knowledge, in particular concerning definitions of 'natural' and 'normal' behaviour, with important consequences for the embodied experiences of women.

Benefits of off-campus education for students in the health sciences: a text-mining analysis.

PubMed

Nakagawa, Kazumasa; Asakawa, Yasuyoshi; Yamada, Keiko; Ushikubo, Mitsuko; Yoshida, Tohru; Yamaguchi, Haruyasu

2012-08-28

In Japan, few community-based approaches have been adopted in health-care professional education, and the appropriate content for such approaches has not been clarified. In establishing community-based education for health-care professionals, clarification of its learning effects is required. A community-based educational program was started in 2009 in the health sciences course at Gunma University, and one of the main elements in this program is conducting classes outside school. The purpose of this study was to investigate using text-analysis methods how the off-campus program affects students. In all, 116 self-assessment worksheets submitted by students after participating in the off-campus classes were decomposed into words. The extracted words were carefully selected from the perspective of contained meaning or content. With the selected terms, the relations to each word were analyzed by means of cluster analysis. Cluster analysis was used to select and divide 32 extracted words into four clusters: cluster 1-"actually/direct," "learn/watch/hear," "how," "experience/participation," "local residents," "atmosphere in community-based clinical care settings," "favorable," "communication/conversation," and "study"; cluster 2-"work of staff member" and "role"; cluster 3-"interaction/communication," "understanding," "feel," "significant/important/necessity," and "think"; and cluster 4-"community," "confusing," "enjoyable," "proactive," "knowledge," "academic knowledge," and "class." The students who participated in the program achieved different types of learning through the off-campus classes. They also had a positive impression of the community-based experience and interaction with the local residents, which is considered a favorable outcome. Off-campus programs could be a useful educational approach for students in health sciences.

Knowledge Discovery, Integration and Communication for Extreme Weather and Flood Resilience Using Artificial Intelligence: Flood AI Alpha

NASA Astrophysics Data System (ADS)

Demir, I.; Sermet, M. Y.

2016-12-01

Nobody is immune from extreme events or natural hazards that can lead to large-scale consequences for the nation and public. One of the solutions to reduce the impacts of extreme events is to invest in improving resilience with the ability to better prepare, plan, recover, and adapt to disasters. The National Research Council (NRC) report discusses the topic of how to increase resilience to extreme events through a vision of resilient nation in the year 2030. The report highlights the importance of data, information, gaps and knowledge challenges that needs to be addressed, and suggests every individual to access the risk and vulnerability information to make their communities more resilient. This abstracts presents our project on developing a resilience framework for flooding to improve societal preparedness with objectives; (a) develop a generalized ontology for extreme events with primary focus on flooding; (b) develop a knowledge engine with voice recognition, artificial intelligence, natural language processing, and inference engine. The knowledge engine will utilize the flood ontology and concepts to connect user input to relevant knowledge discovery outputs on flooding; (c) develop a data acquisition and processing framework from existing environmental observations, forecast models, and social networks. The system will utilize the framework, capabilities and user base of the Iowa Flood Information System (IFIS) to populate and test the system; (d) develop a communication framework to support user interaction and delivery of information to users. The interaction and delivery channels will include voice and text input via web-based system (e.g. IFIS), agent-based bots (e.g. Microsoft Skype, Facebook Messenger), smartphone and augmented reality applications (e.g. smart assistant), and automated web workflows (e.g. IFTTT, CloudWork) to open the knowledge discovery for flooding to thousands of community extensible web workflows.

Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain.

PubMed

Goldstein, Ayelet; Shahar, Yuval; Orenbuch, Efrat; Cohen, Matan J

2017-10-01

To examine the feasibility of the automated creation of meaningful free-text summaries of longitudinal clinical records, using a new general methodology that we had recently developed; and to assess the potential benefits to the clinical decision-making process of using such a method to generate draft letters that can be further manually enhanced by clinicians. We had previously developed a system, CliniText (CTXT), for automated summarization in free text of longitudinal medical records, using a clinical knowledge base. In the current study, we created an Intensive Care Unit (ICU) clinical knowledge base, assisted by two ICU clinical experts in an academic tertiary hospital. The CTXT system generated free-text summary letters from the data of 31 different patients, which were compared to the respective original physician-composed discharge letters. The main evaluation measures were (1) relative completeness, quantifying the data items missed by one of the letters but included by the other, and their importance; (2) quality parameters, such as readability; (3) functional performance, assessed by the time needed, by three clinicians reading each of the summaries, to answer five key questions, based on the discharge letter (e.g., "What are the patient's current respiratory requirements?"), and by the correctness of the clinicians' answers. Completeness: In 13/31 (42%) of the letters the number of important items missed in the CTXT-generated letter was actually less than or equal to the number of important items missed by the MD-composed letter. In each of the MD-composed letters, at least two important items that were mentioned by the CTXT system were missed (a mean of 7.2±5.74). In addition, the standard deviation in the number of missed items in the MD letters (STD=15.4) was much higher than the standard deviation in the CTXT-generated letters (STD=5.3). Quality: The MD-composed letters obtained a significantly better grade in three out of four measured parameters. However, the standard variation in the quality of the MD-composed letters was much greater than the standard variation in the quality of the CTXT-generated letters (STD=6.25 vs. STD=2.57, respectively). Functional evaluation: The clinicians answered the five questions on average 40% faster (p<0.001) when using the CTXT-generated letters than when using the MD-composed letters. In four out of the five questions the clinicians' correctness was equal to or significantly better (p<0.005) when using the CTXT-generated letters than when using the MD-composed letters. An automatic knowledge-based summarization system, such as the CTXT system, has the capability to model complex clinical domains, such as the ICU, and to support interpretation and summarization tasks such as the creation of a discharge summary letter. Based on the results, we suggest that the use of such systems could potentially enhance the standardization of the letters, significantly increase their completeness, and reduce the time to write the discharge summary. The results also suggest that using the resultant structured letters might reduce the decision time, and enhance the decision quality, of decisions made by other clinicians. Copyright © 2017 Elsevier B.V. All rights reserved.

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records.

PubMed

Jackson, Richard; Patel, Rashmi; Velupillai, Sumithra; Gkotsis, George; Hoyle, David; Stewart, Robert

2018-01-01

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge.  Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

PubMed Central

Jackson, Richard; Patel, Rashmi; Velupillai, Sumithra; Gkotsis, George; Hoyle, David; Stewart, Robert

2018-01-01

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge.  Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions. PMID:29899974

EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.

PubMed

Boland, Mary Regina; Tu, Samson W; Carini, Simona; Sim, Ida; Weng, Chunhua

2012-01-01

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria.

Narrative Inquiry for Science Education: Teachers' repertoire-making in the case of environmental curriculum

NASA Astrophysics Data System (ADS)

Hwang, Seyoung

2011-04-01

This paper considers how the school science curriculum can be conceptualised in order to address the contingent and complex nature of environmental and sustainability-related knowledge and understanding. A special concern lies in the development of research perspectives and tools for investigating ways, in which teachers are faced with complex and various situations in the sense-making of science-related issues, and subsequent pedagogic issues. Based on an empirical examination of Korean teachers' sense-making of their curricular practice, the paper develops a narrative approach to teachers' perspectives and knowledge by considering the value of stories as sense-making tools for reflective questioning of what is worth teaching, how and why. By employing the idea of 'repertoire', the study regards teachers' stories about their environment-related personal and teaching experiences as offering angles with which to understand teachers' motivation and reflection in curricular development and implementation. Furthermore, three empirical cases present ways in which the nature of knowledge and understanding is recognised and potentially integrated into pedagogies through teachers' narratives. Finally, the paper argues for the need to reconsider the role of the science teacher in addressing environmental and sustainability-related issues, in ways that facilitate teachers' reflexive interpretation of meanings in cultural texts and the construction of pedagogic text.

Knowledge representation and management: benefits and challenges of the semantic web for the fields of KRM and NLP.

PubMed

Rassinoux, A-M

2011-01-01

To summarize excellent current research in the field of knowledge representation and management (KRM). A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched. This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources. Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.

Strategy Shifts during Learning from Texts and Pictures

ERIC Educational Resources Information Center

Schnotz, Wolfgang; Ludewig, Ulrich; Ullrich, Mark; Horz, Holger; McElvany, Nele; Baumert, Jürgen

2014-01-01

Reading for learning frequently requires integrating text and picture information into coherent knowledge structures. This article presents an experimental study aimed at analyzing the strategies used by students for integrating text and picture information. Four combinations of texts and pictures (text-picture units) were selected from textbooks…

Building Background Knowledge through Reading: Rethinking Text Sets

ERIC Educational Resources Information Center

Lupo, Sarah M.; Strong, John Z.; Lewis, William; Walpole, Sharon; McKenna, Michael C.

2018-01-01

To increase reading volume and help students access challenging texts, the authors propose a four-dimensional framework for text sets. The quad text set framework is designed around a target text: a challenging content area text, such as a canonical literary work, research article, or historical primary source document. The three remaining…

Towards iconic language for patient records, drug monographs, guidelines and medical search engines.

PubMed

Lamy, Jean-Baptiste; Duclos, Catherine; Hamek, Saliha; Beuscart-Zéphir, Marie-Catherine; Kerdelhué, Gaetan; Darmoni, Stefan; Favre, Madeleine; Falcoff, Hector; Simon, Christian; Pereira, Suzanne; Serrot, Elisabeth; Mitouard, Thierry; Hardouin, Etienne; Kergosien, Yannick; Venot, Alain

2010-01-01

Practicing physicians have limited time for consulting medical knowledge and records. We have previously shown that using icons instead of text to present drug monographs may allow contraindications and adverse effects to be identified more rapidly and more accurately. These findings were based on the use of an iconic language designed for drug knowledge, providing icons for many medical concepts, including diseases, antecedents, drug classes and tests. In this paper, we describe a new project aimed at extending this iconic language, and exploring the possible applications of these icons in medicine. Based on evaluators' comments, focus groups of physicians and opinions of academic, industrial and associative partners, we propose iconic applications related to patient records, for example summarizing patient conditions, searching for specific clinical documents and helping to code structured data. Other applications involve the presentation of clinical practice guidelines and improving the interface of medical search engines. These new applications could use the same iconic language that was designed for drug knowledge, with a few additional items that respect the logic of the language.

A unified approach to the design of clinical reporting systems.

PubMed

Gouveia-Oliveira, A; Salgado, N C; Azevedo, A P; Lopes, L; Raposo, V D; Almeida, I; de Melo, F G

1994-12-01

Computer-based Clinical Reporting Systems (CRS) for diagnostic departments that use structured data entry have a number of functional and structural affinities suggesting that a common software architecture for CRS may be defined. Such an architecture should allow easy expandability and reusability of a CRS. We report the development methodology and the architecture of SISCOPE, a CRS originally designed for gastrointestinal endoscopy that is expandable and reusable. Its main components are a patient database, a knowledge base, a reports base, and screen and reporting engines. The knowledge base contains the description of the controlled vocabulary and all the information necessary to control the menu system, and is easily accessed and modified with a conventional text editor. The structure of the controlled vocabulary is formally presented as an entity-relationship diagram. The screen engine drives a dynamic user interface and the reporting engine automatically creates a medical report; both engines operate by following a set of rules and the information contained in the knowledge base. Clinical experience has shown this architecture to be highly flexible and to allow frequent modifications of both the vocabulary and the menu system. This structure provided increased collaboration among development teams, insulating the domain expert from the details of the database, and enabling him to modify the system as necessary and to test the changes immediately. The system has also been reused in several different domains.

Beyond accuracy: creating interoperable and scalable text-mining web services.

PubMed

Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2016-06-15

The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl : Zhiyong.Lu@nih.gov. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

How students deal with inconsistencies in health knowledge.

PubMed

Bientzle, Martina; Cress, Ulrike; Kimmerle, Joachim

2013-07-01

In their work, health care professionals have to deal daily with inconsistent health information and are confronted with differing therapeutic health concepts. Medical education should prepare students to handle these challenges adequately. The aim of this study was to contribute to a better understanding of how students deal with inconsistencies in health knowledge when they are presented with either a therapeutic concept they accept or one they reject. Seventy-six students of physiotherapy participated in this 2 × 2 experiment with health information (consistent versus inconsistent information) and therapeutic concept (congruent versus contradictory therapeutic concept) as between-group factors. The participants' task was to improve the quality of a text about the effectiveness of stretching; participants were randomly assigned to one of four texts. Knowledge acquisition and text modification were measured as dependent variables. Students acquired more knowledge when they worked with a text containing inconsistent information. Medical information that was presented in agreement with a student's therapeutic concept was also more readily acquired than the same information presented posing a contradictory therapeutic concept. Participants modified the contradictory text in order to adapt it to their own point of view. Disagreement resulted in a disregard or devaluation of the information itself, which in turn was detrimental to learning. It is a problem when prospective health care professionals turn a blind eye to discrepancies that do not fit their view of the world. It may be useful for educational purposes to include a knowledge conflict caused by a combination of conviction and inconsistent information to facilitate learning processes. © 2013 John Wiley & Sons Ltd.

Integrating Relational Reasoning and Knowledge Revision during Reading

ERIC Educational Resources Information Center

Kendeou, Panayiota; Butterfuss, Reese; Van Boekel, Martin; O'Brien, Edward J.

2017-01-01

Our goal in this theoretical contribution is to connect research on knowledge revision and relational reasoning. To achieve this goal, first, we review the "knowledge revision components framework" (KReC) that provides an account of knowledge revision processes, specifically as they unfold during reading of texts. Second, we review a…

Unsupervised chunking based on graph propagation from bilingual corpus.

PubMed

Zhu, Ling; Wong, Derek F; Chao, Lidia S

2014-01-01

This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms of F-score.

Automatic indexing of scanned documents: a layout-based approach

NASA Astrophysics Data System (ADS)

Esser, Daniel; Schuster, Daniel; Muthmann, Klemens; Berger, Michael; Schill, Alexander

2012-01-01

Archiving official written documents such as invoices, reminders and account statements in business and private area gets more and more important. Creating appropriate index entries for document archives like sender's name, creation date or document number is a tedious manual work. We present a novel approach to handle automatic indexing of documents based on generic positional extraction of index terms. For this purpose we apply the knowledge of document templates stored in a common full text search index to find index positions that were successfully extracted in the past.

Artificial intelligence in cardiology.

PubMed

Bonderman, Diana

2017-12-01

Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiology are reviewed. The text also touches on the ethical issues and speculates on the future roles of automated algorithms versus clinicians in cardiology and medicine in general.

Secondary School Advanced Mathematics, Chapter 1, Organizing Geometric Knowledge, Chapter 2, Concepts and Skills in Algebra. Student's Text.

ERIC Educational Resources Information Center

Stanford Univ., CA. School Mathematics Study Group.

This text is the first of five in the Secondary School Advanced Mathematics (SSAM) series which was designed to meet the needs of students who have completed the Secondary School Mathematics (SSM) program, and wish to continue their study of mathematics. The first chapter, devoted to organizing geometric knowledge, deals with the distinction…

Investigating the Effect of Contextual Clues on the Processing of Unfamiliar Words in Second Language Listening Comprehension

ERIC Educational Resources Information Center

Cai, Wei; Lee, Benny P. H.

2010-01-01

This study examines the effect of contextual clues on the use of strategies (inferencing and ignoring) and knowledge sources (semantics, morphology, world knowledge, and others) for processing unfamiliar words in listening comprehension. Three types of words were investigated: words with local co-text clues, global co-text clues and extra-textual…

The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

ERIC Educational Resources Information Center

Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

2013-01-01

The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

A Pilot Study of the Naming Transaction Shell

DTIC Science & Technology

1991-06-01

effective computer-based instructional design. AIDA will take established theories of knowledge, learning , and instruction and incorporate the theories...felt that anyone could learn to use the system both in design and delivery modes. Traditional course development (non- computer instruction) for the...students were studying and learning the material in the text. This often resulted in wasted effort in the simulator. By ensuring that the students knew the

Sparse Forward-Backward for Fast Training of Conditional Random Fields

DTIC Science & Technology

2006-01-01

knowledge- based systems. Proceedings of the 6th Conference on Uncertainty in Artifcial Intelligence , 1990. Appears to be unavailable. [4] Michael I...response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and...task, the NetTalk text-to-speech data set [5], we can now train a conditional random field (CRF) in about 6 hours for which training previously

Making sense of the ECG: cases for self-assessment Making Sense of the ECG: Cases for Self-Assessment Houghton Andrew and Gray David Hodder Education £18.99 290pp 9780340946893 034094689X [Formula: see text].

PubMed

2011-02-10

This practical pocket-book approach to electrocardiogram (ECG) interpretation accompanies Making sense of the eCg by the same authors. it is also designed to be used alone to test knowledge of ECG interpretation and to make clinical decisions based on presented scenarios.

New challenges for text mining: mapping between text and manually curated pathways

PubMed Central

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

Attention, motivation, and reading coherence failure: a neuropsychological perspective.

PubMed

Wasserman, Theodore

2012-01-01

Reading coherence, defined as the ability to create appropriate, meaningful connections between the elements within a specific text itself and between elements within a text and the reader's prior knowledge, is one of the key processes involved in reading comprehension. This article describes reading coherence within the context of a neuropsychological model combining recent research in motivation, attention, and working memory. Specifically, a unique neuropsychologically identifiable form of reading coherence failure arising from the attentional and motivational deficiencies, based in altered frontoventral striatal reward circuits associated with noradrenaline (NA) circuitry, consistent with the delay-aversion model (dual-pathway model) of Sonuga-Barke ( 2003 ) is postulated. This article provides a model for this subset of reading disorders of which etiology is based on the executive support processes for reading and not in the mechanics of actual reading such as decoding and phonetics.

Contextual Text Mining

ERIC Educational Resources Information Center

Mei, Qiaozhu

2009-01-01

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

Comprehension and Learning from Refutation and Expository Texts

ERIC Educational Resources Information Center

Diakidoy, Irene-Anna N.; Mouskounti, Thalia; Ioannides, Christos

2011-01-01

The study compared the effects of a refutation text on comprehension and learning outcomes to those of a standard expository text. Undergraduate students with varying amounts of accurate and inaccurate prior knowledge read and recalled a refutation or an expository text about energy. Comprehension measures included the amount of text information…

Automation and integration of components for generalized semantic markup of electronic medical texts.

PubMed

Dugan, J M; Berrios, D C; Liu, X; Kim, D K; Kaizer, H; Fagan, L M

1999-01-01

Our group has built an information retrieval system based on a complex semantic markup of medical textbooks. We describe the construction of a set of web-based knowledge-acquisition tools that expedites the collection and maintenance of the concepts required for text markup and the search interface required for information retrieval from the marked text. In the text markup system, domain experts (DEs) identify sections of text that contain one or more elements from a finite set of concepts. End users can then query the text using a predefined set of questions, each of which identifies a subset of complementary concepts. The search process matches that subset of concepts to relevant points in the text. The current process requires that the DE invest significant time to generate the required concepts and questions. We propose a new system--called ACQUIRE (Acquisition of Concepts and Queries in an Integrated Retrieval Environment)--that assists a DE in two essential tasks in the text-markup process. First, it helps her to develop, edit, and maintain the concept model: the set of concepts with which she marks the text. Second, ACQUIRE helps her to develop a query model: the set of specific questions that end users can later use to search the marked text. The DE incorporates concepts from the concept model when she creates the questions in the query model. The major benefit of the ACQUIRE system is a reduction in the time and effort required for the text-markup process. We compared the process of concept- and query-model creation using ACQUIRE to the process used in previous work by rebuilding two existing models that we previously constructed manually. We observed a significant decrease in the time required to build and maintain the concept and query models.

Review of recommendations for the management of dental trauma presented in first-aid textbooks and manuals.

PubMed

Emerich, Katarzyna; Gazda, Ewa

2010-06-01

To be able to help at the site of the oro-facial injury, the majority of persons would turn to medical books and first-aid books to extend their knowledge. Proper information in first-aid textbooks and manuals should be the best way to present necessary procedures on how to act at the site of injury. The objective of this review is to report the quality of the knowledge presented in first-aid books and manuals. We carried out a review of first-aid international textbooks and manuals available in Medical University Libraries in Poland. The inclusion criteria were all manuals on first-aid that were written for medical staff and lay persons, and were published between 1969 and 2007. All texts were screened for dental trauma treatment recommendations. Our literature review has shown that among 45 first-aid textbooks and manuals only 19 mention procedures for use in case of dental trauma. Of those texts, only 13 detail the storage media for an avulsed tooth until replantation. Current, evidence-based, recommendations concerning first-aid procedures after dental trauma should be incorporated in forthcoming editions of first-aid textbooks and manuals. The guidance on procedures contained in reviewed texts is misleading.

Learning from physics text: A synthesis of recent research

NASA Astrophysics Data System (ADS)

Alexander, Patricia A.; Kulikowich, Jonna M.

Learning from physics text is described as a complex interaction of learner, text, and context variables. As a multidimensional procedure, text processing in the domain of physics relies on readers' knowledge and interest, and on readers' ability to monitor or regulate their processing. Certain textual features intended to assist readers in understanding and remembering physics content may actually work to the detriment of those very processes. Inclusion of seductive details and the incorporation of analogies may misdirect readers' attention or may increase processing demands, particularly in those cases when readers' physics knowledge is low. The questioning behaviors of teachers also impact on the task of comprehending physics texts. Finally, within the context of the classroom, the information that teachers dispense or the materials they employ can significantly influence the process of learning from physics text.

Executive Function, Self-Regulated Learning, and Reading Comprehension: A Training Study.

PubMed

Cirino, Paul T; Miciak, Jeremy; Gerst, Elyssa; Barnes, Marcia A; Vaughn, Sharon; Child, Amanda; Huston-Warren, Emily

The goal of this study was to evaluate the extent to which training that emphasizes the process of executive function (EF) and self-regulated learning (SRL) would result in increased reading comprehension; we also evaluated interrelationships of EF, SRL, and reading. We report an experiment ( N = 75 fourth graders) that contrasted two researcher-implemented conditions (text-based reading [TB] and text-based reading plus executive function [TB+EF]) to a control. We also evaluated relationships among measures of SRL, EF, and reading. Both the TB and TB+EF groups outperformed the control group for proximal text comprehension (where the topic was similar to that covered in training) and background knowledge related to it, but the two researcher-led groups performed similarly. There were no significant differences for less proximal text, and again similar performance for both TB and TB+EF. Correlations among measures were weak in general, although the pattern was similar to that found in the extant literature. The findings speak to the difficulty in separating these components from those of strong instruction more generally. The relationships of these constructs to reading comprehension will likely be enhanced by more sensitive measurement of EF and reading comprehension, particularly where tied to active treatment components.

Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

PubMed

Elayavilli, Ravikumar Komandur; Liu, Hongfang

2016-01-01

Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological framework. The ICEPO ontology is available for download at http://openbionlp.org/mutd/supplementarydata/ICEPO/ICEPO.owl.

Guiding Students through Expository Text with Text Feature Walks

ERIC Educational Resources Information Center

Kelley, Michelle J.; Clausen-Grace, Nicki

2010-01-01

The Text Feature Walk is a structure created and employed by the authors that guides students in the reading of text features in order to access prior knowledge, make connections, and set a purpose for reading expository text. Results from a pilot study are described in order to illustrate the benefits of using the Text Feature Walk over…

Science Teacher Efficacy and Extrinsic Factors Toward Professional Development Using Video Games in a Design-Based Research Model: The Next Generation of STEM Learning

NASA Astrophysics Data System (ADS)

Annetta, Leonard A.; Frazier, Wendy M.; Folta, Elizabeth; Holmes, Shawn; Lamb, Richard; Cheng, Meng-Tzu

2013-02-01

Designed-based research principles guided the study of 51 secondary-science teachers in the second year of a 3-year professional development project. The project entailed the creation of student-centered, inquiry-based, science, video games. A professional development model appropriate for infusing innovative technologies into standards-based curricula was employed to determine how science teacher's attitudes and efficacy where impacted while designing science-based video games. The study's mixed-method design ascertained teacher efficacy on five factors (General computer use, Science Learning, Inquiry Teaching and Learning, Synchronous chat/text, and Playing Video Games) related to technology and gaming using a web-based survey). Qualitative data in the form of online blog posts was gathered during the project to assist in the triangulation and assessment of teacher efficacy. Data analyses consisted of an Analysis of Variance and serial coding of teacher reflective responses. Results indicated participants who used computers daily have higher efficacy while using inquiry-based teaching methods and science teaching and learning. Additional emergent findings revealed possible motivating factors for efficacy. This professional development project was focused on inquiry as a pedagogical strategy, standard-based science learning as means to develop content knowledge, and creating video games as technological knowledge. The project was consistent with the Technological Pedagogical Content Knowledge (TPCK) framework where overlapping circles of the three components indicates development of an integrated understanding of the suggested relationships. Findings provide suggestions for development of standards-based science education software, its integration into the curriculum and, strategies for implementing technology into teaching practices.

Specialized Knowledge Representation and the Parameterization of Context.

PubMed

Faber, Pamela; León-Araúz, Pilar

2016-01-01

Though instrumental in numerous disciplines, context has no universally accepted definition. In specialized knowledge resources it is timely and necessary to parameterize context with a view to more effectively facilitating knowledge representation, understanding, and acquisition, the main aims of terminological knowledge bases. This entails distinguishing different types of context as well as how they interact with each other. This is not a simple objective to achieve despite the fact that specialized discourse does not have as many contextual variables as those in general language (i.e., figurative meaning, irony, etc.). Even in specialized text, context is an extremely complex concept. In fact, contextual information can be specified in terms of scope or according to the type of information conveyed. It can be a textual excerpt or a whole document; a pragmatic convention or a whole culture; a concrete situation or a prototypical scenario. Although these versions of context are useful for the users of terminological resources, such resources rarely support context modeling. In this paper, we propose a taxonomy of context primarily based on scope (local and global) and further divided into syntactic, semantic, and pragmatic facets. These facets cover the specification of different types of terminological information, such as predicate-argument structure, collocations, semantic relations, term variants, grammatical and lexical cohesion, communicative situations, subject fields, and cultures.

Specialized Knowledge Representation and the Parameterization of Context

PubMed Central

Faber, Pamela

2016-01-01

Though instrumental in numerous disciplines, context has no universally accepted definition. In specialized knowledge resources it is timely and necessary to parameterize context with a view to more effectively facilitating knowledge representation, understanding, and acquisition, the main aims of terminological knowledge bases. This entails distinguishing different types of context as well as how they interact with each other. This is not a simple objective to achieve despite the fact that specialized discourse does not have as many contextual variables as those in general language (i.e., figurative meaning, irony, etc.). Even in specialized text, context is an extremely complex concept. In fact, contextual information can be specified in terms of scope or according to the type of information conveyed. It can be a textual excerpt or a whole document; a pragmatic convention or a whole culture; a concrete situation or a prototypical scenario. Although these versions of context are useful for the users of terminological resources, such resources rarely support context modeling. In this paper, we propose a taxonomy of context primarily based on scope (local and global) and further divided into syntactic, semantic, and pragmatic facets. These facets cover the specification of different types of terminological information, such as predicate-argument structure, collocations, semantic relations, term variants, grammatical and lexical cohesion, communicative situations, subject fields, and cultures. PMID:26941674

Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Shabarekh, Charlotte

2017-05-01

Today's battlefields are shifting to "denied areas", where the use of U.S. Military air and ground assets is limited. To succeed, the U.S. intelligence analysts increasingly rely on available open-source intelligence (OSINT) which is fraught with inconsistencies, biased reporting and fake news. Analysts need automated tools for retrieval of information from OSINT sources, and these solutions must identify and resolve conflicting and deceptive information. In this paper, we present a misinformation detection model (MDM) which converts text to attributed knowledge graphs and runs graph-based analytics to identify misinformation. At the core of our solution is identification of knowledge conflicts in the fused multi-source knowledge graph, and semi-supervised learning to compute locally consistent reliability and credibility scores for the documents and sources, respectively. We present validation of proposed method using an open source dataset constructed from the online investigations of MH17 downing in Eastern Ukraine.

Recent progress in automatically extracting information from the pharmacogenomic literature

PubMed Central

Garten, Yael; Coulet, Adrien; Altman, Russ B

2011-01-01

The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports. We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies. Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature. These methods enable extraction of specific types of information and can also provide answers to general, systemic queries. In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications. PMID:21047206

Conceptual Coherence, Comprehension, and Vocabulary Acquisition: A Knowledge Effect?

ERIC Educational Resources Information Center

Cervetti, Gina N.; Wright, Tanya S.; Hwang, HyeJin

2016-01-01

Previous research has documented the role of readers' existing topic knowledge in supporting students' comprehension of text; yet, we know less about how to build students' knowledge in order to support comprehension and vocabulary learning. In the current study, we test the hypothesis that knowledge can be built and leveraged simultaneously in…

Toward Independent L2 Readers: Effects of Text Adjuncts, Subject Knowledge, L1 Reading, and L2 Proficiency

ERIC Educational Resources Information Center

Brantmeier, Cindy; Hammadou Sullivan, JoAnn; Strube, Michael

2014-01-01

With 97 learners in an advanced Spanish course, the study examines the effects of textual enhancement adjuncts, prior subject knowledge, first language (L1) reading ability, and second language (L2) Spanish proficiency on L2 comprehension of scientific passages. Readings included two texts with two types of embedded questions: a pause or written…

Reading in the Disciplines: The Challenges of Adolescent Literacy. Final Report from Carnegie Corporation of New York's Council on Advancing Adolescent Literacy

ERIC Educational Resources Information Center

Lee, Carol D.; Spratley, Anika

2010-01-01

Adolescents may struggle with text for a number of reasons, including problems with a) vocabulary knowledge, b) general knowledge of topics and text structures, c) knowing of what to do when comprehension breaks down, or d) proficiency in monitoring their own reading comprehension. Most recent literacy initiatives target younger readers and…

Tides of Change: New Themes and Questions in Workplace Learning.

ERIC Educational Resources Information Center

Fenwick, Tara

2001-01-01

Current issues in workplace learning scholarship include the following: situated learning and knowledge, culture and context of workplaces, texts and discourses mediating working knowledge, identity and difference, and equity and ethics in working knowledge. (Contains 39 references.) (SK)

Calculating semantic relatedness for biomedical use in a knowledge-poor environment.

PubMed

Rybinski, Maciej; Aldana-Montes, José

2014-01-01

Computing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a 'closed' problem. We propose a method in which computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts. As keyterm extraction is a costly process, it is done in a preprocessing step on a 'per-document' basis in order to limit the on-line processing. The actual computations are executed in a compact vector space, limited by the most informative extraction results. The method has been evaluated on five direct benchmarks by calculating correlation coefficients w.r.t. average human answers. It also has been used on Gene - Disease and Disease- Disease data pairs to highlight its potential use as a data analysis tool. Apart from comparisons with reported results, some interesting features of the method have been studied, i.e. the relationship between result quality, efficiency and applicable trimming threshold for size reduction. Experimental evaluation shows that the presented method obtains results that are comparable with current state of the art methods, even surpassing them on a majority of the benchmarks. Additionally, a possible usage scenario for the method is showcased with a real-world data experiment. Our method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources.

Comparative effectiveness of a serious game and an e-module to support patient safety knowledge and awareness.

PubMed

Dankbaar, Mary E W; Richters, Olivier; Kalkman, Cor J; Prins, Gerrie; Ten Cate, Olle T J; van Merrienboer, Jeroen J G; Schuit, Stephanie C E

2017-02-02

Serious games have the potential to teach complex cognitive skills in an engaging way, at relatively low costs. Their flexibility in use and scalability makes them an attractive learning tool, but more research is needed on the effectiveness of serious games compared to more traditional formats such e-modules. We investigated whether undergraduate medical students developed better knowledge and awareness and were more motivated after learning about patient-safety through a serious game than peers who studied the same topics using an e-module. Fourth-year medical students were randomly assigned to either a serious game that included video-lectures, biofeedback exercises and patient missions (n = 32) or an e-module, that included text-based lectures on the same topics (n = 34). A third group acted as a historical control-group without extra education (n = 37). After the intervention, which took place during the clinical introduction course, before the start of the first rotation, all students completed a knowledge test, a self-efficacy test and a motivation questionnaire. During the following 10-week clinical rotation they filled out weekly questionnaires on patient-safety awareness and stress. The results showed patient safety knowledge had equally improved in the game group and e-module group compared to controls, who received no extra education. Average learning-time was 3 h for the game and 1 h for the e-module-group. The serious game was evaluated as more engaging; the e-module as more easy to use. During rotations, students in the three groups reported low and similar levels of patient-safety awareness and stress. Students who had treated patients successfully during game missions experienced higher self-efficacy and less stress during their rotation than students who treated patients unsuccessfully. Video-lectures (in a game) and text-based lectures (in an e-module) can be equally effective in developing knowledge on specific topics. Although serious games are strongly engaging for students and stimulate them to study longer, they do not necessarily result in better performance in patient safety issues.

Evaluation of a cartoon-based knowledge dissemination intervention on scientific and ethical challenges raised by nutrigenomics/nutrigenetics research.

PubMed

Lafrenière, Darquise; Hurlimann, Thierry; Menuz, Vincent; Godard, Béatrice

2014-10-01

The push for knowledge translation on the part of health research funding agencies is significant in Canada, and many strategies have been adopted to promote the conversion of knowledge into action. In recent years, an increasing number of health researchers have been studying arts-based interventions to transform knowledge into action. This article reports on the results of an online questionnaire aimed at evaluating the effectiveness of a knowledge dissemination intervention (KDI) conveying findings from a study on the scientific and ethical challenges raised by nutrigenomics-nutrigenetics (NGx) research. The KDI was based on the use of four Web pages combining original, interactive cartoon-like illustrations accompanied by text to disseminate findings to Canadian Research Ethics Boards members, as well as to NGx researchers and researchers in ethics worldwide. Between May and October 2012, the links to the Web pages were sent in a personal email to target audience members, one thematic Web page at a time. On each thematic Web page, members of the target audience were invited to answer nine evaluation questions assessing the effectiveness of the KDI on four criteria, (i) acquisition of knowledge; (ii) change in initial understanding; (iii) generation of questions from the findings; and (iv) intent to change own practice. Response rate was low; results indicate that: (i) content of the four Web pages did not bring new knowledge to a majority of the respondents, (ii) initial understanding of the findings did not change for a majority of NGx researchers and a minority of ethics respondents, (iii) although the KDI did raise questions for respondents, it did not move them to change their practice. While target end-users may not feel that they actually learned from the KDI, it seems that the findings conveyed encouraged reflection and raised useful and valuable questions for them. Moreover, the evaluation of the KDI proved to be useful to gain knowledge about our target audiences' views since respondents' comments allowed us to improve our understanding of the disseminated knowledge as well as to modify (and hopefully improve) the content of the Web pages used for dissemination. Copyright © 2014 Elsevier Ltd. All rights reserved.

SNAD: Sequence Name Annotation-based Designer.

PubMed

Sidorov, Igor A; Reshetov, Denis A; Gorbalenya, Alexander E

2009-08-14

A growing diversity of biological data is tagged with unique identifiers (UIDs) associated with polynucleotides and proteins to ensure efficient computer-mediated data storage, maintenance, and processing. These identifiers, which are not informative for most people, are often substituted by biologically meaningful names in various presentations to facilitate utilization and dissemination of sequence-based knowledge. This substitution is commonly done manually that may be a tedious exercise prone to mistakes and omissions. Here we introduce SNAD (Sequence Name Annotation-based Designer) that mediates automatic conversion of sequence UIDs (associated with multiple alignment or phylogenetic tree, or supplied as plain text list) into biologically meaningful names and acronyms. This conversion is directed by precompiled or user-defined templates that exploit wealth of annotation available in cognate entries of external databases. Using examples, we demonstrate how this tool can be used to generate names for practical purposes, particularly in virology. A tool for controllable annotation-based conversion of sequence UIDs into biologically meaningful names and acronyms has been developed and placed into service, fostering links between quality of sequence annotation, and efficiency of communication and knowledge dissemination among researchers.

Presentation of Diagnostic Information to Doctors May Change Their Interpretation and Clinical Management: A Web-Based Randomised Controlled Trial.

PubMed

Ben-Shlomo, Yoav; Collin, Simon M; Quekett, James; Sterne, Jonathan A C; Whiting, Penny

2015-01-01

There is little evidence on how best to present diagnostic information to doctors and whether this makes any difference to clinical management. We undertook a randomised controlled trial to see if different data presentations altered clinicians' decision to further investigate or treat a patient with a fictitious disorder ("Green syndrome") and their ability to determine post-test probability. We recruited doctors registered with the United Kingdom's largest online network for medical doctors between 10 July and 6" November 2012. Participants were randomised to one of four arms: (a) text summary of sensitivity and specificity, (b) Fagan's nomogram, (c) probability-modifying plot (PMP), (d) natural frequency tree (NFT). The main outcome measure was the decision whether to treat, not treat or undertake a brain biopsy on the hypothetical patient and the correct post-test probability. Secondary outcome measures included knowledge of diagnostic tests. 917 participants attempted the survey and complete data were available from 874 (95.3%). Doctors randomized to the PMP and NFT arms were more likely to treat the patient than those randomized to the text-only arm. (ORs 1.49, 95% CI 1.02, 2.16) and 1.43, 95% CI 0.98, 2.08 respectively). More patients randomized to the PMP (87/218-39.9%) and NFT (73/207-35.3%) arms than the nomogram (50/194-25.8%) or text only (30/255-11.8%) arms reported the correct post-test probability (p <0.001). Younger age, postgraduate training and higher self-rated confidence all predicted better knowledge performance. Doctors with better knowledge were more likely to view an optional learning tutorial (OR per correct answer 1.18, 95% CI 1.06, 1.31). Presenting diagnostic data using a probability-modifying plot or natural frequency tree influences the threshold for treatment and improves interpretation of tests results compared to text summary of sensitivity and specificity or Fagan's nomogram.

Intelligent Text Retrieval and Knowledge Acquisition from Texts for NASA Applications: Preprocessing Issues

NASA Technical Reports Server (NTRS)

2002-01-01

A system that retrieves problem reports from a NASA database is described. The database is queried with natural language questions. Part-of-speech tags are first assigned to each word in the question using a rule based tagger. A partial parse of the question is then produced with independent sets of deterministic finite state a utomata. Using partial parse information, a look up strategy searches the database for problem reports relevant to the question. A bigram stemmer and irregular verb conjugates have been incorporated into the system to improve accuracy. The system is evaluated by a set of fifty five questions posed by NASA engineers. A discussion of future research is also presented.

Mentoring and Supervision in Healthcare Gopee Neil Mentoring and Supervision in Healthcare Sage Publications £22.99 304pp 9780857024190 0857024191 [Formula: see text].

PubMed

2011-10-26

THIS VOLUME is an excellent resource for experienced and new mentors and preceptors, with Neil Gopee setting out the knowledge base, skills and attitudes for successful mentoring. This second edition encompasses developments in health care and the recent standards and competencies that have been introduced for mentors and supervisors, with advice on how to implement them in clinical practice.

The use of telehealth (text messaging and video communications) in patients with cystic fibrosis: A pilot study.

PubMed

Gur, Michal; Nir, Vered; Teleshov, Anna; Bar-Yoseph, Ronen; Manor, Eynav; Diab, Gizelle; Bentur, Lea

2017-05-01

Background Poor communications between cystic fibrosis (CF) patients and health-care providers may result in gaps in knowledge and misconceptions about medication usage, and can lead to poor adherence. We aimed to assess the feasibility of using WhatsApp and Skype to improve communications. Methods This single-centre pilot study included CF patients who were older than eight years of age assigned to two groups: one without intervention (control group), and one with intervention. Each patient from the intervention group received Skype-based online video chats and WhatsApp messages from members of the multidisciplinary CF team. CF questionnaires, revised (CFQ-R) scores, knowledge and adherence based on CF My Way and patients satisfaction were evaluated before and after three months. Feasibility was assessed by session attendance, acceptability and satisfaction survey. Descriptive analysis and paired and non-paired t-tests were used as applicable. Results Eighteen patients were recruited to this feasibility study (nine in each group). Each intervention group participant had between four and six Skype video chats and received 22-45 WhatsApp messages. In this small study, CFQ-R scores, knowledge, adherence and patient satisfaction were similar in both groups before and after the three-month intervention. Conclusions A telehealth-based approach, using Skype video chats and WhatsApp messages, was feasible and acceptable in this pilot study. A larger and longer multi-centre study is warranted to examine the efficacy of these interventions to improve knowledge, adherence and communication.

Protecting Information

NASA Astrophysics Data System (ADS)

Loepp, Susan; Wootters, William K.

2006-09-01

For many everyday transmissions, it is essential to protect digital information from noise or eavesdropping. This undergraduate introduction to error correction and cryptography is unique in devoting several chapters to quantum cryptography and quantum computing, thus providing a context in which ideas from mathematics and physics meet. By covering such topics as Shor's quantum factoring algorithm, this text informs the reader about current thinking in quantum information theory and encourages an appreciation of the connections between mathematics and science.Of particular interest are the potential impacts of quantum physics:(i) a quantum computer, if built, could crack our currently used public-key cryptosystems; and (ii) quantum cryptography promises to provide an alternative to these cryptosystems, basing its security on the laws of nature rather than on computational complexity. No prior knowledge of quantum mechanics is assumed, but students should have a basic knowledge of complex numbers, vectors, and matrices. Accessible to readers familiar with matrix algebra, vector spaces and complex numbers First undergraduate text to cover cryptography, error-correction, and quantum computation together Features exercises designed to enhance understanding, including a number of computational problems, available from www.cambridge.org/9780521534765

Producing "science/fictions" about the rural and urban poor: Community-based learning at a medical college in South India

NASA Astrophysics Data System (ADS)

Arur, Aditi Ashok

This dissertation is an ethnographic case study of a community-based teaching program (CBTP) in public health at a medical college in South India that explored how the CBTP produced particular ways of seeing and understanding rural and urban poor communities. Drawing from critical, feminist, and postcolonial scholars, I suggest that the knowledge produced in the CBTP can be understood as "science/fictions", that is, as cultural texts shaped by transnational development discourses as well as medical teachers' and students' sociospatial imaginations of the rural and urban poor. I explored how these science/fictions mediated medical students' performative actions and interactions with a rural and an urban poor community in the context of the CBTP. At the same time, I also examined how knowledge produced in students' encounters with these communities disrupted their naturalized understandings about these communities, and how it was taken up to renarrativize science/fictions anew. Data collection and analyses procedures were informed by critical ethnographic and critical discourse analysis approaches. Data sources includes field notes constructed from observations of the CBTP, interviews with medical teachers and students, and curricular texts including the standardized national textbook of public health. The findings of this study illustrate how the CBTP staged the government and technology as central actors in the production of healthy bodies, communities, and environments, and implicitly positioned medical teachers and students as productive citizens of a modern nation while rural and urban poor communities were characterized sometimes as empowered, and at other times as not-yet-modern and in need of reform. However, the community also constituted an alternate pedagogical site of engagement in that students' encounters with community members disrupted students' assumptions about these communities to an extent. Nevertheless, institutionalized practices of assessment, and epistemological and ontological understandings of the nature of science tended to privilege the standardized curriculum and popular cultural stereotypes as scientific knowledge thereby excluding the place-based narratives of local communities, medical students, and teachers. This study, therefore, argues that interactions with local communities in community-based education and development programs cannot democratize knowledge production in medical education without a simultaneous engagement with post-foundational epistemologies in the social sciences and humanities.

Artificial muscles' enrichment text: Chemical Literacy Profile of pre-service teachers

NASA Astrophysics Data System (ADS)

Hernani, Ulum, Luthfi Lulul; Mudzakir, Ahmad

2017-08-01

This research aims to determine the profile of chemical literacy abilities of pre-service teachers based on scientific attitudes and scientific competencies in PISA 2015 through individualized learning by using an artificial muscle context based-enrichment book. This research uses descriptive method, involving 20 of the 90 randomly selected population. This research uses a multiple-choice questions instrument. The result of this research are : 1) in the attitude aspects of interest in science and technology, valuing scientific approaches to inquiry, and environmental awareness, the results obtained respectively for 90%, 80%, and 30%. 2) for scientific competence of apply appropriate scientific knowledge, identify models and representations, make appropriate predictions, and explain the potential implications of scientific knowledge for society, the results obtained respectively for 30%, 50%, 60%, and 55%. 3) For scientific competence of identify the question explored in a given scientific study and distinguish questions that could be investigated scientifically, the results obtained respectively for 30 % and 50%. 4) For scientific competence of transform data from one representation to another and draw appropriate conclusions, the results obtained respectively for 60% and 45%. Based on the results, which need to be developed in pre-service chemistry teachers are environmental awareness, apply appropriate scientific knowledge, identify the question explored in a given scientific study, and draw appropriate conclusions.

Texting Adolescents in Repeat DKA and Their Caregivers.

PubMed

Wagner, David V; Barry, Samantha; Teplitsky, Lena; Sheffield, Annan; Stoeckel, Maggie; Ogden, Jimmie D; Karkula, Elizabeth; Hartman, Alexandra; Duke, Danny C; Spiro, Kim; Harris, Michael A

2016-07-01

Text message interventions are feasible, preferable, and sometimes effective for youth with diabetes. However, few, if any studies, have examined the personalized use of text messages with youth repeatedly hospitalized for diabetic ketoacidosis (DKA) and their caregivers. This study characterizes the use of personalized text messages in Novel Interventions in Children's Healthcare (NICH). Approximately 2 months of text messages sent to youth with repeat DKA and their caregivers were logged regarding the following text characteristics: (1) content, (2) intervention type, (3) timing, and (4) recipient characteristics. NICH interventionists sent 2.3 and 1.5 texts per day to patients and caregivers, respectively. Approximately 59% of outgoing texts occurred outside of typical business hours, and roughly 68% of texts contained some form of support and/or encouragement. The relation between type of intended intervention and day/time of text was significant, χ(2)(2, N = 5,808) = 266.93, P < .001. Interventionists were more likely to send behavioral intervention text messages outside of business hours, whereas they were more likely to send care coordination and case management text messages during business hours. To our knowledge, this is the first study to specifically categorize and describe the personalized use of text messages with youth repeatedly hospitalized for DKA and their caregivers. Findings indicate that a promising treatment program for these youth frequently used text interventions to deliver praise and encouragement to patients and caregivers alike, often outside of typical business hours, and tailored text content based on patient and caregiver characteristics. © 2016 Diabetes Technology Society.

Inflammatory Bowel Disease Telemedicine Clinical Trial: Impact of Educational Text Messages on Disease-Specific Knowledge Over 1 Year.

PubMed

Abutaleb, Ameer; Buchwald, Andrea; Chudy-Onwugaje, Kenechukwu; Langenberg, Patricia; Regueiro, Miguel; Schwartz, David A; Tracy, J Kathleen; Ghazi, Leyla; Patil, Seema A; Quezada, Sandra M; Russman, Katharine M; Quinn, Charlene C; Jambaulikar, Guruprasad; Beaulieu, Dawn B; Horst, Sara; Cross, Raymond K

2018-05-18

Effective treatments are available for patients with inflammatory bowel disease (IBD); however, suboptimal outcomes occur and are often linked to patients' limited disease knowledge. The aim of this analysis was to determine if delivery of educational messages through a telemedicine system improves IBD knowledge. TELEmedicine for Patients with IBD (TELE-IBD) was a randomized controlled trial with visits at baseline, 6 months, and 12 months; patient knowledge was a secondary aim of the study. Patients were randomized to receive TELE-IBD every other week (EOW), weekly (TELE-IBD W), or standard of care. Knowledge was assessed at each visit with the Crohn's and Colitis Knowledge (CCKNOW) survey. The primary outcome was change in CCKNOW score over 1 year compared between the TELE-IBD and control groups. This analysis included 219 participants. Participants in the TELE-IBD arms had a greater improvement in CCKNOW score compared with standard care (TELE-IBD EOW +2.4 vs standard care +1.8, P = 0.03; TELE-IBD W +2.0 vs standard care +1.8, P = 0.35). Participants with lower baseline CCKNOW scores had a greater change in their score over time (P < 0.01). However, after adjusting for race, site, and baseline knowledge, there was no difference in CCKNOW score change between the control and telemedicine arms. Telemedicine improves IBD-specific knowledge through text messaging, although the improvement is not additive with greater frequency of text messages. However, after adjustment for confounding variables, telemedicine is not superior to education given through standard visits at referral centers. Further research is needed to determine if revised systems with different modes of delivery and/or frequency of messages improve disease knowledge.

Semantic text relatedness on Al-Qur’an translation using modified path based method

NASA Astrophysics Data System (ADS)

Irwanto, Yudi; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Abdul Baquee Muhammad [1] have built Corpus that contained AlQur’an domain, WordNet and dictionary. He has did initialisation in the development of knowledges about AlQur’an and the knowledges about relatedness between texts in AlQur’an. The Path based measurement method that proposed by Liu, Zhou and Zheng [3] has never been used in the AlQur’an domain. By using AlQur’an translation dataset in this research, the path based measurement method proposed by Liu, Zhou and Zheng [3] will be used to test this method in AlQur’an domain to obtain similarity values and to measure its correlation value. In this study the degree value is proposed to be used in modifying the path based method that proposed in previous research. Degree Value is the number of links that owned by a lcs (lowest common subsumer) node on a taxonomy. The links owned by a node on the taxonomy represent the semantic relationship that a node has in the taxonomy. By using degree value to modify the path-based method that proposed in previous research is expected that the correlation value obtained will increase. After running some experiment by using proposed method, the correlation measurement value can obtain fairly good correlation ties with 200 Word Pairs derive from Noun POS SimLex-999. The correlation value that be obtained is 93.3% which means their bonds are strong and they have very strong correlation. Whereas for the POS other than Noun POS vocabulary that owned by WordNet is incomplete therefore many pairs of words that the value of its similarity is zero so the correlation value is low.

Transforming Knowledge in Undergraduate Teacher Education. A Craft Paper 91-1.

ERIC Educational Resources Information Center

Reid, Gem

This paper examines the teacher educator's role in assisting prospective teachers to grasp what it means to transform knowledge so that content and pedagogy intersect. Knowledge is not certain nor is its authority held in the text or the teacher's lesson plans. If students perceive knowledge to be open-ended, requiring vital curiosity, they will…

Argumentation Text Construction by Japanese as a Foreign Language Writers: A Dynamic View of Transfer

ERIC Educational Resources Information Center

Rinnert, Carol; Kobauashi, Hiroe; Katayama, Akemi

2015-01-01

This study takes a dynamic view of transfer as reusing and reshaping previous knowledge in new writing contexts to investigate how novice Japanese as a foreign language (JFL) writers draw on knowledge across languages to construct L1 and L2 texts. We analyzed L1 English and L2 Japanese argumentation essays by the same JFL writers (N = 19) and L1…

Mobile phone text messaging to improve knowledge and practice of diabetic foot care in a developing country: Feasibility and outcomes.

PubMed

Hassan, Zeinab M

2017-06-01

To test the feasibility and effectiveness of using mobile phone text messaging to reinforce learning and the practice of diabetic foot care in a developing country. Ongoing learning reinforcement (2-3 times weekly) by text messaging followed an informal class on diabetic foot care in a community clinic setting. Subjects with cell phone access and no history of diabetic foot wounds or current wounds were recruited for participation (N = 225). Foot examinations and pretesting by survey occurred just before patients departed the clinic; the posttest survey and a final foot examination occurred 12 weeks later. The survey included basic demographic items along with items to measure knowledge and current foot care practices. One sample t tests (raw scores) and Wilcoxon signed-rank tests compared knowledge and practice before and after intervention. Initially, a majority of the sample (76%) reported poor levels of foot care. After 12 weeks <1% reported poor foot care practices. Statistical testing showed significant gains in knowledge (by score and level) and nearly unanimous compliance with daily foot examination. Mobile phone text messaging is an economical, feasible, and effective method for educators to improve diabetic self-care, even in a developing country. © 2017 John Wiley & Sons Australia, Ltd.

Individual Variation in Children's Reading Comprehension across Digital Text Types

ERIC Educational Resources Information Center

Fesel, Sabine S.; Segers, Eliane; Verhoeven, Ludo

2018-01-01

The present study examined children's digital text comprehension of digital text types linear digital text vs hypertext, with or without graphical navigable overviews. We investigated to what extent individual variation in children's comprehension could be explained by lexical quality (word reading efficiency and vocabulary knowledge), cognitive…

Creating Vocative Texts

ERIC Educational Resources Information Center

Nicol, Jennifer J.

2008-01-01

Vocative texts are expressive poetic texts that strive to show rather than tell, that communicate felt knowledge, and that appeal to the senses. They are increasingly used by researchers to present qualitative findings, but little has been written about how to create such texts. To this end, excerpts from an inquiry into the experience and meaning…

Differential Competencies Contributing to Children's Comprehension of Narrative and Expository Texts

ERIC Educational Resources Information Center

Best, Rachel M.; Floyd, Randy G.; Mcnamara, Danielle S.

2008-01-01

This study examined the influences of reading decoding skills and world knowledge on third graders' comprehension of narrative and expository texts. Children read a narrative text and an expository text. Comprehension of each text was assessed with a free recall prompt, three cued recall prompts, and 12 multiple-choice questions. Tests from the…

The modality and redundancy effects in multimedia learning in children with dyslexia.

PubMed

Knoop-van Campen, Carolien A N; Segers, Eliane; Verhoeven, Ludo

2018-05-01

The present study aimed to examine the modality and redundancy effects in multimedia learning in children with dyslexia in order to find out whether their learning benefits from written and/or spoken text with pictures. We compared study time and knowledge gain in 26 11-year-old children with dyslexia and 38 typically reading peers in a within-subjects design. All children were presented with a series of user-paced multimedia lessons in 3 conditions: pictorial information presented with (a) written text, (b) audio, or (c) combined text and audio. We also examined whether children's learning outcomes were related to their working memory. With respect to study time, we found modality and reversed redundancy effects. Children with dyslexia spent more time learning in the text condition, compared with the audio condition and the combined text-and-audio condition. Regarding knowledge gain, no modality or redundancy effects were evidenced. Although the groups differed on working memory, it did not influence the modality or redundancy effect on study time or knowledge gain. In multimedia learning, it thus is more efficient to provide children with dyslexia with audio or with auditory support. Copyright © 2018 John Wiley & Sons, Ltd.

Knowledge and expectations of women undergoing cancer genetic risk assessment: a qualitative analysis of free-text questionnaire comments.

PubMed

Phelps, C; Wood, F; Bennett, P; Brain, K; Gray, J

2007-08-01

Individuals undergoing cancer genetic risk assessment have been found to have a poor understanding of the process, which may affect how well they cope with learning their risk. This paper reports free-text data from questionnaires completed by women undergoing a randomised controlled trial of a psychological intervention. Of the 268 women undergoing genetic assessment for familial breast/ovarian cancer risk who were invited to take part in the trial, 157 women returned research questionnaires. Of these, 97 women provided free-text comments upon referral to a cancer genetics clinic, 62 provided comments whilst waiting for risk information (average, moderate or high), and 36 women provided comments following notification of risk. This paper reports a thematic analysis of the free-text data. Themes reflected individuals' poor knowledge and uncertainty about genetic risk assessment. How well individuals responded to learning their risk depended upon whether expectations had been met. Regardless of risk, individuals undergoing cancer genetic risk assessment are likely to benefit from increased information about its process and timescales, and access to increased psychological support. Free-text comments can provide valuable data about individuals' expectations and knowledge of genetics services.

SureChEMBL: a large-scale, chemically annotated patent document database.

PubMed

Papadatos, George; Davies, Mark; Dedman, Nathan; Chambers, Jon; Gaulton, Anna; Siddle, James; Koks, Richard; Irvine, Sean A; Pettersson, Joe; Goncharoff, Nicko; Hersey, Anne; Overington, John P

2016-01-04

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

SureChEMBL: a large-scale, chemically annotated patent document database

PubMed Central

Papadatos, George; Davies, Mark; Dedman, Nathan; Chambers, Jon; Gaulton, Anna; Siddle, James; Koks, Richard; Irvine, Sean A.; Pettersson, Joe; Goncharoff, Nicko; Hersey, Anne; Overington, John P.

2016-01-01

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/. PMID:26582922

A randomised controlled trial of a web-based educational program in child mental health for schoolteachers.

PubMed

Pereira, Celina Andrade; Wen, Chao Lung; Miguel, Eurípedes Constantino; Polanczyk, Guilherme V

2015-08-01

Children affected by mental disorders are largely unrecognised and untreated across the world. Community resources, including the school system and teachers, are important elements in actions directed to promoting child mental health and preventing and treating mental disorders, especially in low- and middle-income countries. We developed a web-based program to educate primary school teachers on mental disorders in childhood and conducted a cluster-randomised controlled trial to test the effectiveness of the web-based program intervention in comparison with the same program based on text and video materials only and to a waiting-list control group. All nine schools of a single city in the state of São Paulo, Brazil, were randomised to the three groups, and teachers completed the educational programs during 3 weeks. Data were analysed according to complete cases and intention-to-treat approaches. In terms of gains of knowledge about mental disorders, the web-based program intervention was superior to the intervention with text and video materials, and to the waiting-list control group. In terms of beliefs and attitudes about mental disorders, the web-based program intervention group presented less stigmatised concepts than the text and video group and more non-stigmatised concepts than the waiting-list group. No differences were detected in terms of teachers' attitudes. This study demonstrated initial data on the effectiveness of a web-based program in educating schoolteachers on child mental disorders. Future studies are necessary to replicate and extend the findings.

Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations.

PubMed

Robson, Barry; Boray, Srinidhi

2016-06-01

Extracting medical knowledge by structured data mining of many medical records and from unstructured data mining of natural language source text on the Internet will become increasingly important for clinical decision support. Output from these sources can be transformed into large numbers of elements of knowledge in a Knowledge Representation Store (KRS), here using the notation and to some extent the algebraic principles of the Q-UEL Web-based universal exchange and inference language described previously, rooted in Dirac notation from quantum mechanics and linguistic theory. In a KRS, semantic structures or statements about the world of interest to medicine are analogous to natural language sentences seen as formed from noun phrases separated by verbs, prepositions and other descriptions of relationships. A convenient method of testing and better curating these elements of knowledge is by having the computer use them to take the test of a multiple choice medical licensing examination. It is a venture which perhaps tells us almost as much about the reasoning of students and examiners as it does about the requirements for Artificial Intelligence as employed in clinical decision making. It emphasizes the role of context and of contextual probabilities as opposed to the more familiar intrinsic probabilities, and of a preliminary form of logic that we call presyllogistic reasoning. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Representation of knowledge in respiratory medicine: ontology should help the coding process].

PubMed

Blanc, F-X; Baneyx, A; Charlet, J; Housset, B

2010-09-01

Access to medical knowledge is a major issue for health professionals and requires the development of terminologies. The objective of the reported work was to construct an ontology of respiratory medicine, i.e. an organized and formalized terminology composed by specific knowledge. The purpose is to help the medico-economical coding process and to represent the relevant knowledge about the patient. Our researches cover the whole life cycle of an ontology, from the development of a methodology, to building it from texts, to its use in an operational system. A computerized tool, based on the ontology, allows both a medico-economical coding and a graphical medical one. This second one will be used to index hospital reports. Our ontology counts 1913 concepts and contains all the knowledge included in the PMSI part of the SPLF thesaurus. Our tool has been evaluated and showed a recall of 80% and an accuracy of 85% regarding the medico-economical coding. The work presented in this paper justifies the approach that has been used. It must be continued on a large scale to validate our coding principles and the possibility of making enquiries on patient reports concerning clinical research. Copyright © 2010. Published by Elsevier Masson SAS.

Development and design of an intervention to improve physical activity in pregnant women using Text4baby.

PubMed

Huberty, Jennifer; Rowedder, Lacey; Hekler, Eric; Adams, Marc; Hanigan, Emily; McClain, Darya; Balluff, Mary; Buman, Matt; Bushar, Jessica

2016-06-01

Text4baby is a free, mobile health information service for pregnant and post-partum women. This study aims to understand preferences of physical activity text messages (SMS), sequentially develop prototype SMS, and determine preferred dose of SMS to inform a future study utilizing Text4baby. This study had a user-centered design with three phases: (1) literature review and interviews with pregnant women for development of prototype SMS, (2) interviews with health care professionals and pregnant women for prototype SMS feedback, and (3) survey to determine preferred dose of SMS. Data from interviews identified knowledge and support as major themes. Prototypes were developed (N = 14) and informed 168 SMS. Pregnant women (N = 326) thought three SMS/week were about right (50.2 %) and preferred three SMS/week throughout pregnancy (71.9 %). There is a need for opportunities for behavioral scientists to incorporate evidence-based practices within scalable interventions. As such, this research will inform utilization of Text4baby to potentially improve physical activity participation.

Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing

PubMed Central

2013-01-01

Background A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. Results In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Conclusions We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. PMID:23742147

Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles.

PubMed

Xu, Rong; Wang, QuanQiu

2015-02-01

Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications. Copyright © 2014 Elsevier Inc. All rights reserved.

Texting atopic dermatitis patients to optimize learning and eczema area and severity index scores: A pilot randomized control trial.

PubMed

Singer, Hannah M; Levin, Laura E; Morel, Kimberly D; Garzon, Maria C; Stockwell, Melissa S; Lauren, Christine T

2018-05-02

Atopic dermatitis is a common, chronic, debilitating disease. Poor adherence to treatment is the most important preventable contributor to adverse outcomes. Thus, improving adherence can improve patient outcomes. Text message reminders with embedded condition-specific information have been shown to improve pediatric immunization adherence but have not been assessed in atopic dermatitis. The objective was to assess the effect of daily text messages on Eczema Area Severity Index scores and caregiver knowledge of atopic dermatitis. In this pilot randomized controlled trial, caregivers of children with atopic dermatitis enrolled during their initial appointment with a pediatric dermatologist and randomized 1:1 to standard care or daily text messages with patient education material and treatment reminders. Participants completed a multiple-choice atopic dermatitis knowledge quiz at initial and follow-up visits, and Eczema Area Severity Index scores were assessed. Forty-two patients enrolled, and 30 completed the study: 16 standard care group, 14 text message group. There was no significant difference in Eczema Area Severity Index score between the standard care and text message groups at follow-up, with mean decreases in Eczema Area Severity Index score of 53% and 58%, respectively. Mean score on follow-up atopic dermatitis knowledge quiz was significantly higher in the text message group (84% correct) than in the standard care group (75% correct) (P = .04). This pilot study did not demonstrate a difference in Eczema Area Severity Index scores with text message reminders. The significantly higher follow-up atopic dermatitis quiz score in the text message group indicates that participants read and retained information from text messages. Limitations include small sample size and short duration of follow-up. © 2018 Wiley Periodicals, Inc.

Interactive knowledge networks for interdisciplinary course navigation within Moodle.

PubMed

Scherl, Andre; Dethleffsen, Kathrin; Meyer, Michael

2012-12-01

Web-based hypermedia learning environments are widely used in modern education and seem particularly well suited for interdisciplinary learning. Previous work has identified guidance through these complex environments as a crucial problem of their acceptance and efficiency. We reasoned that map-based navigation might provide straightforward and effortless orientation. To achieve this, we developed a clickable and user-oriented concept map-based navigation plugin. This tool is implemented as an extension of Moodle, a widely used learning management system. It visualizes inner and interdisciplinary relations between learning objects and is generated dynamically depending on user set parameters and interactions. This plugin leaves the choice of navigation type to the user and supports direct guidance. Previously developed and evaluated face-to-face interdisciplinary learning materials bridging physiology and physics courses of a medical curriculum were integrated as learning objects, the relations of which were defined by metadata. Learning objects included text pages, self-assessments, videos, animations, and simulations. In a field study, we analyzed the effects of this learning environment on physiology and physics knowledge as well as the transfer ability of third-term medical students. Data were generated from pre- and posttest questionnaires and from tracking student navigation. Use of the hypermedia environment resulted in a significant increase of knowledge and transfer capability. Furthermore, the efficiency of learning was enhanced. We conclude that hypermedia environments based on Moodle and enriched by concept map-based navigation tools can significantly support interdisciplinary learning. Implementation of adaptivity may further strengthen this approach.

Learning and teaching for an ecological sense of place: Toward environmental/science education praxis

NASA Astrophysics Data System (ADS)

Hug, J. William

1998-09-01

This research presents a teaching model designed to enable learners to construct a highly developed ecological perspective and sense of place. The contextually-based research process draws upon scientific and indigenous knowledge from multiple data sources including: autobiographical experiences, environmental literature, science and environmental education research, historical approaches to environmental education, and phenomenological accounts from research participants. Data were analyzed using the theoretical frameworks of qualitative research, hermeneutic phenomenology, heuristics, and constructivism. The resulting model synthesizes and incorporates key educational philosophies and practices from: nature study, resident outdoor education, organized camping, conservation education, environmental education, earth education, outdoor recreation, sustainability, bio-regionalism, deep ecology, ecological and environmental literacy, science and technology in society, and adventure/challenge/experiential education. The model's four components--environmental knowledge, practicing responsible environmental behaviors, community-focused involvement, and direct experience in outdoor settings--contribute in a synergistic way to the development of ecological perspective and a sense of place. The model was honed through experiential use in an environmental science methods course for elementary and secondary prospective science teachers. The instructor/researcher employed individualized instruction, community-based learning, service learning, and the modeling of reflective teaching principles in pursuit of the model's goals. The resulting pedagogical knowledge extends the model's usefulness to such formal and non-formal educational contexts as: elementary/secondary classrooms, nature centers, museums, youth groups, and community organizations. This research has implications for the fields of education, geography, recreation/leisure studies, science teaching, and environmental education. Several aspects of this work make it novel. First, autobiographical and literature-based stories anchor the representations of ecological perspective and sense of place. Second, the dissertation text visually differentiates between story narrative, researcher narrative, and meta-narrative in order to convey the positionality of the researcher's distinct voices. Finally, icons are used throughout the text to visually link the model's multi-dimensional intersections. Oh, and by the way, I hope you read it.

Knowledge Domain and Emerging Trends in Organic Photovoltaic Technology: A Scientometric Review Based on CiteSpace Analysis.

PubMed

Xiao, Fengjun; Li, Chengzhi; Sun, Jiangman; Zhang, Lianjie

2017-01-01

To study the rapid growth of research on organic photovoltaic (OPV) technology, development trends in the relevant research are analyzed based on CiteSpace software of text mining and visualization in scientific literature. By this analytical method, the outputs and cooperation of authors, the hot research topics, the vital references and the development trend of OPV are identified and visualized. Different from the traditional review articles by the experts on OPV, this work provides a new method of visualizing information about the development of the OPV technology research over the past decade quantitatively.

Knowledge Domain and Emerging trends in Organic Photovoltaic Technology: A Scientometric Review Based on CiteSpace Analysis

NASA Astrophysics Data System (ADS)

Xiao, Fengjun; Li, Chengzhi; Sun, Jiangman; Zhang, Lianjie

2017-09-01

To study the rapid growth of research on organic photovoltaic (OPV) technology, development trends in the relevant research are analyzed based on CiteSpace software of text mining and visualization in scientific literature. By this analytical method, the outputs and cooperation of authors, the hot research topics, the vital references and the development trend of OPV are identified and visualized. Different from the traditional review articles by the experts on OPV, this work provides a new method of visualizing information about the development of the OPV technology research over the past decade quantitatively.

The Comparative Effects of Prediction/Discussion-Based Learning Cycle, Conceptual Change Text, and Traditional Instructions on Student Understanding of Genetics

NASA Astrophysics Data System (ADS)

Yilmaz, Diba; Tekkaya, Ceren; Sungur, Semra

2011-03-01

The present study examined the comparative effects of a prediction/discussion-based learning cycle, conceptual change text (CCT), and traditional instructions on students' understanding of genetics concepts. A quasi-experimental research design of the pre-test-post-test non-equivalent control group was adopted. The three intact classes, taught by the same science teacher, were randomly assigned as prediction/discussion-based learning cycle class (N = 30), CCT class (N = 25), and traditional class (N = 26). Participants completed the genetics concept test as pre-test, post-test, and delayed post-test to examine the effects of instructional strategies on their genetics understanding and retention. While the dependent variable of this study was students' understanding of genetics, the independent variables were time (Time 1, Time 2, and Time 3) and mode of instruction. The mixed between-within subjects analysis of variance revealed that students in both prediction/discussion-based learning cycle and CCT groups understood the genetics concepts and retained their knowledge significantly better than students in the traditional instruction group.

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

PubMed

Bekhuis, Tanja

2006-04-03

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.

Assigning clinical codes with data-driven concept representation on Dutch clinical free text.

PubMed

Scheurwegs, Elyne; Luyckx, Kim; Luyten, Léon; Goethals, Bart; Daelemans, Walter

2017-05-01

Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and performed procedures during that stay. This paper aims to enrich algorithms for automated clinical coding by taking a data-driven approach and by using unsupervised and semi-supervised techniques for the extraction of multi-word expressions that convey a generalisable medical meaning (referred to as concepts). Several methods for extracting concepts from text are compared, two of which are constructed from a large unannotated corpus of clinical free text. A distributional semantic model (i.c. the word2vec skip-gram model) is used to generalize over concepts and retrieve relations between them. These methods are validated on three sets of patient stay data, in the disease areas of urology, cardiology, and gastroenterology. The datasets are in Dutch, which introduces a limitation on available concept definitions from expert-based ontologies (e.g. UMLS). The results show that when expert-based knowledge in ontologies is unavailable, concepts derived from raw clinical texts are a reliable alternative. Both concepts derived from raw clinical texts perform and concepts derived from expert-created dictionaries outperform a bag-of-words approach in clinical code assignment. Adding features based on tokens that appear in a semantically similar context has a positive influence for predicting diagnostic codes. Furthermore, the experiments indicate that a distributional semantics model can find relations between semantically related concepts in texts but also introduces erroneous and redundant relations, which can undermine clinical coding performance. Copyright © 2017. Published by Elsevier Inc.

Human Processing of Knowledge from Texts: Acquisition, Integration, and Reasoning

DTIC Science & Technology

1979-06-01

comprehension. Norwood, N.J.: Ablex, 1977. Craik , F.I.M., and Lockhart , R. S. Levels of processing : for memory research. Journal of Verbal Learning A...Table 5.9 presents summary data regarding the performance levels and memory and search processes of individual subjects. The first row in Table 5.9...R-2256-ARP A June 1979 ARPA Order No.: 189-1 9020 Cybernetics Technology Human Processing of Knowledge from Texts: Acquisition, Integration, and

Folate/Folic Acid Knowledge, Intake, and Self-Efficacy of College-Aged Women: Impact of Text Messaging and Availability of a Folic Acid-Containing Supplement

ERIC Educational Resources Information Center

Rampersaud, Gail C.; Sokolow, Andrew; Gruspe, Abigail; Colee, James C.; Kauwell, Gail P. A.

2016-01-01

Objective: To evaluate the impact of educational text messages (TMs) on folate/folic acid knowledge and consumption among college-aged women, and to evaluate the impact of providing folic acid supplements on folate/folic acid intake among college-aged women. Participants: A total of 162 women (18-24 years) recruited from a university. Methods: The…

Analogical transfer as guided by an abstraction process: the case of learning by doing in text editing.

PubMed

Sander, E; Richard, J F

1997-11-01

The authors proposed that not only are the first attempts to solve a problem made by analogy but also that progress in learning can be guided by referring to more abstract knowledge, which affords new possibilities. Two experiments investigated this view in a situation of learning how to use a text editor. Experiments 1A to 1C identified the knowledge associated with 3 domains hypothesized as sources of transfer at increasing levels of abstraction (typewriting, writing in general, manipulating objects). Experiment 2 tested whether participants first use their knowledge about typewriting, then about writing in general, and then about manipulating objects. The data showed that the order of acquisition of text editor functions appeared to be strongly related to this hierarchy, supporting the idea that part of learning consists of discovering properties of objects by accessing increasingly general domains.

Insights gained through Marte Meo counselling: experiences of nurses in dementia specific care units.

PubMed

Einang Alnes, Rigmor; Kirkevold, Marit; Skovdahl, Kirsti

2011-06-01

This study sought to uncover what nurses perceived to have learned, during their participation in video supported counselling, based on Marte Meo principles, in four dementia specific care units. This was a descriptive qualitative study. Data were collected through 12 individual and four focus group interviews. In addition, supplementary data from two video recordings and one written log were included. Findings emerged through content analysis and re-examination of the text based on the initial analysis. The nurses experienced that they acquired new knowledge about the residents through Marte Meo Counselling (MMC), resulting in improved capability to interpret the residents` expressions, and increased awareness of the residents' competence. New knowledge about themselves as nurses also emerged; they recognised how their actions entailed consequences for the interaction, in turn making them conscious of the usefulness of taking time, pacing their interactions, maintaining eye contact and describing the situation in words when the interaction took place. This appeared to increase the resident's perception of being able to cope. This study indicates that MMC helped the nurses to gain knowledge about how to improve interactions with residents suffering from dementia. Further research is warranted into the effectiveness of MMC. © 2010 Blackwell Publishing Ltd.

PathText: a text mining integrator for biological pathway visualizations

PubMed Central

Kemper, Brian; Matsuzaki, Takuya; Matsuoka, Yukiko; Tsuruoka, Yoshimasa; Kitano, Hiroaki; Ananiadou, Sophia; Tsujii, Jun'ichi

2010-01-01

Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com. PMID:20529930

Augmenting the Refutation Text Effect with Analogies and Graphics

ERIC Educational Resources Information Center

Danielson, Robert W.; Sinatra, Gale M.; Kendeou, Panayiota

2016-01-01

Refutation texts have been shown to be effective at promoting knowledge revision. It has been suggested that refutation texts are most effective when the misconception and the correct information are co-activated and integrated with causal networks that support the correct information. We explored two augmentations to a refutation text that might…

What Experiences Do Expository Books on Recommended Book Lists Offer to K-2 Students?

ERIC Educational Resources Information Center

Kletzien, Sharon B.; Dreher, Mariam Jean

2017-01-01

Teachers can use expository texts to teach academic vocabulary, content knowledge, text structure, and text features. National associations' recommended book lists are often used to identify books for classrooms. Previously we identified expository texts on these lists from 2001-2002 and 2011-2012. The current study explored instructional…

Guiding Readers to New Understandings through Electronic Text.

ERIC Educational Resources Information Center

Patterson, Nancy, Ed.; Pipkin, Gloria, Ed.

2001-01-01

Argues that computer technology can help to engage struggling readers in meaningful transactions with text. Lists and describes seven web sites that will captivate reluctant readers. Notes three web sites that send students on "WebQuests" to transact with text in order to build knowledge. Discusses other ways to engage students in text via…

An architecture and protocol for communications satellite constellations regarded as multi-agent systems

NASA Technical Reports Server (NTRS)

Lindley, Craig A.

1995-01-01

This paper presents an architecture for satellites regarded as intercommunicating agents. The architecture is based upon a postmodern paradigm of artificial intelligence in which represented knowledge is regarded as text, inference procedures are regarded as social discourse and decision making conventions and the semantics of representations are grounded in the situated behaviour and activity of agents. A particular protocol is described for agent participation in distributed search and retrieval operations conducted as joint activities.

Cluster randomized trial of text message reminders to retail staff in tanzanian drug shops dispensing artemether-lumefantrine: effect on dispenser knowledge and patient adherence.

PubMed

Bruxvoort, Katia; Festo, Charles; Kalolella, Admirabilis; Cairns, Matthew; Lyaruu, Peter; Kenani, Mitya; Kachur, S Patrick; Goodman, Catherine; Schellenberg, David

2014-10-01

Artemisinin combination therapies are available in private outlets, but patient adherence might be compromised by poor advice from dispensers. In this cluster randomized trial in drug shops in Tanzania, 42 of 82 selected shops were randomized to receive text message reminders about what advice to provide when dispensing artemether-lumefantrine (AL). Eligible patients purchasing AL at shops in both arms were followed up at home and questioned about each dose taken. Dispensers were interviewed regarding knowledge of AL dispensing practices and receipt of the malaria-related text messages. We interviewed 904 patients and 110 dispensers from 77 shops. Although there was some improvement in dispenser knowledge, there was no difference between arms in adherence measured as completion of all doses (intervention 68.3%, control 69.8%, p [adjusted] = 0.6), or as completion of each dose at the correct time (intervention 33.1%, control 32.6%, p [adjusted] = 0.9). Further studies on the potential of text messages to improve adherence are needed. © The American Society of Tropical Medicine and Hygiene.

Knowledge Discovery in Textual Documentation: Qualitative and Quantitative Analyses.

ERIC Educational Resources Information Center

Loh, Stanley; De Oliveira, Jose Palazzo M.; Gastal, Fabio Leite

2001-01-01

Presents an application of knowledge discovery in texts (KDT) concerning medical records of a psychiatric hospital. The approach helps physicians to extract knowledge about patients and diseases that may be used for epidemiological studies, for training professionals, and to support physicians to diagnose and evaluate diseases. (Author/AEF)

Instructional Transaction Theory: Knowledge Relationships among Processes, Entities, and Activities.

ERIC Educational Resources Information Center

Merrill, M. David; And Others

1993-01-01

Discussion of instructional transaction theory focuses on knowledge representation in an automated instructional design expert system. A knowledge structure called PEA-Net (processes, entities, and activities) is explained; the refrigeration process is used as an example; text resources and graphic resources are described; and simulations are…

Fostering Topic Knowledge: Essential for Academic Writing

ERIC Educational Resources Information Center

Proske, Antje; Kapp, Felix

2013-01-01

Several researchers emphasize the role of the writer's topic knowledge for writing. In academic writing topic knowledge is often constructed by studying source texts. One possibility to support that essential phase of the writing process is to provide interactive learning questions which facilitate the construction of an adequate situation…

Managing biological networks by using text mining and computer-aided curation

NASA Astrophysics Data System (ADS)

Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

2015-11-01

In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

Naked in the Old and the New World: Differences and Analogies in Descriptions of European and American herbae nudae in the Sixteenth Century.

PubMed

Čermáková, Lucie; Černá, Jana

2018-03-01

The sixteenth century could be understand as a period of renaissance of interest in nature and as a period of development of natural history as a discipline. The spreading of the printing press was connected to the preparation of new editions of Classical texts and to the act of correcting and commenting on these texts. This forced scholars to confront texts with living nature and to subject it to more careful investigation. The discovery of America uncovered new horizons and brought new natural products, which were exotic and unknown to Classical tradition. The aim of this study is to compare strategies and categories, which were used in describing plants of the Old and the New World. Attention will be paid to the first reactions to the new flora, to the methods of naming and describing plants, to the ways of gaining knowledge about plants from local sources or by means of one's own observation. The confrontation with novelty puts naturalists in the Old World and in the New World in a similar situation. It reveals the limits of traditional knowledge based on Classical authorities. A closer investigation, however, brings to light not only the sometimes unexpected similarities, but also the differences which were due to the radical otherness of American plants.

Visual analysis of online social media to open up the investigation of stance phenomena

PubMed Central

Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas; Paradis, Carita; Sahlgren, Magnus

2015-01-01

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool. PMID:29249903

Visual analysis of online social media to open up the investigation of stance phenomena.

PubMed

Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas; Paradis, Carita; Sahlgren, Magnus

2016-04-01

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

Informatics in radiology (infoRAD): HTML and Web site design for the radiologist: a primer.

PubMed

Ryan, Anthony G; Louis, Luck J; Yee, William C

2005-01-01

A Web site has enormous potential as a medium for the radiologist to store, present, and share information in the form of text, images, and video clips. With a modest amount of tutoring and effort, designing a site can be as painless as preparing a Microsoft PowerPoint presentation. The site can then be used as a hub for the development of further offshoots (eg, Web-based tutorials, storage for a teaching library, publication of information about one's practice, and information gathering from a wide variety of sources). By learning the basics of hypertext markup language (HTML), the reader will be able to produce a simple and effective Web page that permits display of text, images, and multimedia files. The process of constructing a Web page can be divided into five steps: (a) creating a basic template with formatted text, (b) adding color, (c) importing images and multimedia files, (d) creating hyperlinks, and (e) uploading one's page to the Internet. This Web page may be used as the basis for a Web-based tutorial comprising text documents and image files already in one's possession. Finally, there are many commercially available packages for Web page design that require no knowledge of HTML.

Executive Function, Self-Regulated Learning, and Reading Comprehension: A Training Study

PubMed Central

Cirino, Paul T.; Miciak, Jeremy; Gerst, Elyssa; Barnes, Marcia A.; Vaughn, Sharon; Child, Amanda; Huston-Warren, Emily

2016-01-01

The goal of this study was to evaluate the extent to which training that emphasizes the process of executive function (EF) and self-regulated learning (SRL) would result in increased reading comprehension; we also evaluated interrelationships of EF, SRL, and reading. We report an experiment (N = 75 fourth graders) that contrasted two researcher-implemented conditions (text-based reading [TB] and text-based reading plus executive function [TB+EF]) to a control. We also evaluated relationships among measures of SRL, EF, and reading. Both the TB and TB+EF groups outperformed the control group for proximal text comprehension (where the topic was similar to that covered in training) and background knowledge related to it, but the two researcher-led groups performed similarly. There were no significant differences for less proximal text, and again similar performance for both TB and TB+EF. Correlations among measures were weak in general, although the pattern was similar to that found in the extant literature. The findings speak to the difficulty in separating these components from those of strong instruction more generally. The relationships of these constructs to reading comprehension will likely be enhanced by more sensitive measurement of EF and reading comprehension, particularly where tied to active treatment components. PMID:26746314

A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

PubMed Central

Basaruddin, T.

2016-01-01

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645. PMID:27843447

Revisiting Fluctuations in L2 Article Choice in L1-Korean L2-English Learners.

PubMed

Sarker, Bijon K; Baek, Seunghyun

2017-04-01

The current study investigated the distinction of L2 (second language) English article choice sensitivity in fifty-three L1-Korean L2-English learners in semantic contexts. In the context of English as a foreign language, the participants were divided into two groups based on grammatical ability as determined by their performance on a cloze test. In addition, a forced-choice elicitation test and a writing production test were administered to assess, respectively, the participants' receptive and productive article choice abilities. Regardless of grammatical ability, the results disclosed the overuse of the indefinite a in the [[Formula: see text]definite, -specific] context and the definite the in the [-definite, [Formula: see text]specific] context on the forced-choice elicitation test. In the [[Formula: see text]definite, [Formula: see text]specific] and [-definite, -specific] contexts, however, the overuse of either the indefinite a or the definite the, respectively, was less likely. Furthermore, it was revealed on the writing test that the participants more accurately used the definite the than the indefinite a, and they were also found to unreasonably omit more articles than to add or substitute articles on the writing production test. The findings across the two tests indicate that L1-Korean L2-English learners are more likely to have intrinsic difficulties transferring their L1 noun phrase (NP) knowledge to L2 NP knowledge owing to structural discrepancies and complex interfaces between L1 NPs and L2 NPs with respect to syntactic, semantic and pragmatic/discourse language subsystems.

Computational methods to extract meaning from text and advance theories of human cognition.

PubMed

McNamara, Danielle S

2011-01-01

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. Copyright © 2010 Cognitive Science Society, Inc.

A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications

PubMed Central

Cameron, Delroy; Bodenreider, Olivier; Yalamanchili, Hima; Danh, Tu; Vallabhaneni, Sreeram; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Rindflesch, Thomas C.

2014-01-01

Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil Hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson has been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson has been developed. Results Our methodology not only recovered the 3 associations commonly recognized as Swanson’s Hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s Hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology for semi- automatically recovering and decomposing Swanson’s RS-DFO Hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). These suggest that three critical aspects of LBD include: 1) the need for more expressive representations beyond Swanson’s ABC model; 2) an ability to accurately extract semantic information from text; and 3) the semantic integration of scientific literature with structured background knowledge. PMID:23026233

Astronomische Konzepte und Jenseitsvorstellungen in den Pyramidentexten.

NASA Astrophysics Data System (ADS)

Krauss, R.

The Egyptian pyramid texts were first written down around 2300 B.C. They are based on the religious belief in a stellar hereafter and contain the astronomical knowledge of their authors. For example, the ecliptical belt was understood as a canal, on which moon and planets cruise; of the latter Mercury, Venus and Mars were identified. On the basis of their visibility in the course of a year the fixed stars were divided into two groups; a southern group with a period of invisibility and a northern group with continual visibility. The ecliptical "canal" was correctly identified as a division line between the two groups of fixed stars. The aim of this book is to bring these texts foreward to a better understanding of their astronomical background.

Mobile phone text messaging interventions for HIV and other chronic diseases: an overview of systematic reviews and framework for evidence transfer.

PubMed

Mbuagbaw, Lawrence; Mursleen, Sara; Lytvyn, Lyubov; Smieja, Marek; Dolovich, Lisa; Thabane, Lehana

2015-01-22

Strong international commitment and the widespread use of antiretroviral therapy have led to higher longevity for people living with human immune deficiency virus (HIV). Text messaging interventions have been shown to improve health outcomes in people living with HIV. The objectives of this overview were to: map the state of the evidence of text messaging interventions, identify knowledge gaps, and develop a framework for the transfer of evidence to other chronic diseases. We conducted a systematic review of systematic reviews on text messaging interventions to improve health or health related outcomes. We conducted a comprehensive search of PubMed, EMBASE (Exerpta Medica Database), CINAHL (Cumulative Index to Nursing and Allied Health Literature), PsycINFO, Web of Science (WoS) and the Cochrane Library on the 17th April 2014. Screening, data extraction and assessment of methodological quality were done in duplicate. Our findings were used to develop a conceptual framework for transfer. Our search identified 135 potential systematic reviews of which nine were included, reporting on 37 source studies, conducted in 19 different countries. Seven of nine (77.7%) of these reviews were high quality. There was some evidence for text messaging as a tool to improve adherence to antiretroviral therapy. Text messages also improved attendance at appointments and behaviour change outcomes. The findings were inconclusive for self-management of illness, treatment of tuberculosis and communicating results of medical investigations. The geographical distribution of text messaging research was limited to specific regions of the world. Prominent knowledge gaps included the absence of data on long term outcomes, patient satisfaction, and economic evaluations. The included reviews also identified methodological limitations in many of the primary studies. Global evidence supports the use of text messaging as a tool to improve adherence to medication and attendance at scheduled appointments. Given the similarities between HIV and other chronic diseases (long-term medications, life-long care, strong link to behaviour and the need for home-based support) evidence from HIV may be transferred to these diseases using our proposed framework by integration of HIV and chronic disease services or direct transfer.

Reflections on 19th-Century Experience with Knowledge Diffusion: The Sixth Annual Howard Davis Memorial Lecture, April 11, 1991.

ERIC Educational Resources Information Center

Weiss, Carol H.

1991-01-01

Provides the text of the Howard Davis Memorial Lecture, which was presented to the Knowledge Utilization Society in 1991. The lecture describes the work of the Society for the Diffusion of Useful Knowledge, which was active in Great Britain the nineteenth century and compares it with current practices in the field of knowledge utilization. (12…

Web-Based Collaborative Publications System: R&Tserve

NASA Technical Reports Server (NTRS)

Abrams, Steve

1997-01-01

R&Tserve is a publications system based on 'commercial, off-the-shelf' (COTS) software that provides a persistent, collaborative workspace for authors and editors to support the entire publication development process from initial submission, through iterative editing in a hierarchical approval structure, and on to 'publication' on the WWW. It requires no specific knowledge of the WWW (beyond basic use) or HyperText Markup Language (HTML). Graphics and URLs are automatically supported. The system includes a transaction archive, a comments utility, help functionality, automated graphics conversion, automated table generation, and an email-based notification system. It may be configured and administered via the WWW and can support publications ranging from single page documents to multiple-volume 'tomes'.

Effectiveness of Mind Mapping Technique in Information Retrieval Among Medical College Students in Puducherry-A Pilot Study.

PubMed

Kalyanasundaram, Madhanraj; Abraham, Sherin Billy; Ramachandran, Divija; Jayaseelan, Venkatachalam; Bazroy, Joy; Singh, Zile; Purty, Anil Jacob

2017-01-01

The traditional teaching learning methods involve a one way process of transmission of knowledge leaving the students lacking behind in creative abilities. Medical schools need to change their teaching strategies to keep the interest of students and empower them for future self- learning and critical thinking. To assess the impact of mind mapping technique in information retrieval among medical college students in Puducherry. A pilot study was conducted using experimental study design among sixth semester MBBS students ( n = 64) at a medical college in Puducherry, India. One group ( n = 32) followed the text reading method and another group ( n = 32) followed the mind mapping technique to learn the same passage given to them. The knowledge about the topic was assessed using a pre designed questionnaire at baseline, day 0 and day 7. The knowledge gain is the primary outcome variable and is compared between two groups. The feedback regarding the teaching methods was obtained from the participants. Mean knowledge score in the text group was lesser than the mind map group at baseline (2.6 Vs 3.5; p = 0.08). On Day 0, the mean score in text group was slightly lesser than the mind map group (8.7 Vs 9.0; p = 0.26). On Day 7, the mean score in mind map group is significantly more than the text group (8.9 Vs 8.5; p = 0.03). The mind mapping technique is an innovative and effective method in remembering things better than the routine way of reading texts.

Effectiveness of Mind Mapping Technique in Information Retrieval Among Medical College Students in Puducherry-A Pilot Study

PubMed Central

Kalyanasundaram, Madhanraj; Abraham, Sherin Billy; Ramachandran, Divija; Jayaseelan, Venkatachalam; Bazroy, Joy; Singh, Zile; Purty, Anil Jacob

2017-01-01

Background: The traditional teaching learning methods involve a one way process of transmission of knowledge leaving the students lacking behind in creative abilities. Medical schools need to change their teaching strategies to keep the interest of students and empower them for future self- learning and critical thinking. Objective: To assess the impact of mind mapping technique in information retrieval among medical college students in Puducherry. Methods: A pilot study was conducted using experimental study design among sixth semester MBBS students (n = 64) at a medical college in Puducherry, India. One group (n = 32) followed the text reading method and another group (n = 32) followed the mind mapping technique to learn the same passage given to them. The knowledge about the topic was assessed using a pre designed questionnaire at baseline, day 0 and day 7. The knowledge gain is the primary outcome variable and is compared between two groups. The feedback regarding the teaching methods was obtained from the participants. Results: Mean knowledge score in the text group was lesser than the mind map group at baseline (2.6 Vs 3.5; p = 0.08). On Day 0, the mean score in text group was slightly lesser than the mind map group (8.7 Vs 9.0; p = 0.26). On Day 7, the mean score in mind map group is significantly more than the text group (8.9 Vs 8.5; p = 0.03). Conclusion: The mind mapping technique is an innovative and effective method in remembering things better than the routine way of reading texts. PMID:28331249

Tools for Knowledge Analysis, Synthesis, and Sharing

NASA Astrophysics Data System (ADS)

Medland, Michael B.

2007-04-01

Change and complexity are creating a need for increasing levels of literacy in science and technology. Presently, we are beginning to provide students with clear contexts in which to learn, including clearly written text, visual displays and maps, and more effective instruction. We are also beginning to give students tools that promote their own literacy by helping them to interact with the learning context. These tools include peer-group skills as well as strategies to analyze text and to indicate comprehension by way of text summaries and concept maps. Even with these tools, more appears to be needed. Disparate backgrounds and languages interfere with the comprehension and the sharing of knowledge. To meet this need, two new tools are proposed. The first tool fractures language ontologically, giving all learners who use it a language to talk about what has, and what has not, been uttered in text or talk about the world. The second fractures language epistemologically, giving those involved in working with text or on the world around them a way to talk about what they have done and what remains to be done. Together, these tools operate as a two- tiered knowledge representation of knowledge. This representation promotes both an individual meta-cognitive and a social meta-cognitive approach to what is known and to what is not known, both ontologically and epistemologically. Two hypotheses guide the presentation: If the tools are taught during early childhood, children will be prepared to master science and technology content. If the tools are used by both students and those who design and deliver instruction, the learning of such content will be accelerated.

How concept images affect students' interpretations of Newton's method

NASA Astrophysics Data System (ADS)

Engelke Infante, Nicole; Murphy, Kristen; Glenn, Celeste; Sealey, Vicki

2018-07-01

Knowing when students have the prerequisite knowledge to be able to read and understand a mathematical text is a perennial concern for instructors. Using text describing Newton's method and Vinner's notion of concept image, we exemplify how prerequisite knowledge influences understanding. Through clinical interviews with first-semester calculus students, we determined how evoked concept images of tangent lines and roots contributed to students' interpretation and application of Newton's method. Results show that some students' concept images of root and tangent line developed throughout the interview process, and most students were able to adequately interpret the text on Newton's method. However, students with insufficient concept images of tangent line and students who were unwilling or unable to modify their concept images of tangent line after reading the text were not successful in interpreting Newton's method.

User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system.

PubMed

Quiñones, Karin D; Su, Hua; Marshall, Byron; Eggers, Shauna; Chen, Hsinchun

2007-09-01

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques

PubMed Central

Kavuluru, Ramakanth; Han, Sifei; Harris, Daniel

2017-01-01

Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient’s medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult. PMID:28748227

Augmenting Oracle Text with the UMLS for enhanced searching of free-text medical reports.

PubMed

Ding, Jing; Erdal, Selnur; Dhaval, Rakesh; Kamal, Jyoti

2007-10-11

The intrinsic complexity of free-text medical reports imposes great challenges for information retrieval systems. We have developed a prototype search engine for retrieving clinical reports that leverages the powerful indexing and querying capabilities of Oracle Text, and the rich biomedical domain knowledge and semantic structures that are captured in the UMLS Metathesaurus.

Text Coherence in Translation

ERIC Educational Resources Information Center

Zheng, Yanping

2009-01-01

In the thesis a coherent text is defined as a continuity of senses of the outcome of combining concepts and relations into a network composed of knowledge space centered around main topics. And the author maintains that in order to obtain the coherence of a target language text from a source text during the process of translation, a translator can…

Automation and integration of components for generalized semantic markup of electronic medical texts.

PubMed Central

Dugan, J. M.; Berrios, D. C.; Liu, X.; Kim, D. K.; Kaizer, H.; Fagan, L. M.

1999-01-01

Our group has built an information retrieval system based on a complex semantic markup of medical textbooks. We describe the construction of a set of web-based knowledge-acquisition tools that expedites the collection and maintenance of the concepts required for text markup and the search interface required for information retrieval from the marked text. In the text markup system, domain experts (DEs) identify sections of text that contain one or more elements from a finite set of concepts. End users can then query the text using a predefined set of questions, each of which identifies a subset of complementary concepts. The search process matches that subset of concepts to relevant points in the text. The current process requires that the DE invest significant time to generate the required concepts and questions. We propose a new system--called ACQUIRE (Acquisition of Concepts and Queries in an Integrated Retrieval Environment)--that assists a DE in two essential tasks in the text-markup process. First, it helps her to develop, edit, and maintain the concept model: the set of concepts with which she marks the text. Second, ACQUIRE helps her to develop a query model: the set of specific questions that end users can later use to search the marked text. The DE incorporates concepts from the concept model when she creates the questions in the query model. The major benefit of the ACQUIRE system is a reduction in the time and effort required for the text-markup process. We compared the process of concept- and query-model creation using ACQUIRE to the process used in previous work by rebuilding two existing models that we previously constructed manually. We observed a significant decrease in the time required to build and maintain the concept and query models. Images Figure 1 Figure 2 Figure 4 Figure 5 PMID:10566457

Prior Knowledge and Story Processing: Integration, Selection, and Variation. Technical Report No. 138.

ERIC Educational Resources Information Center

Spiro, Rand J.

Psychological research concerning several aspects of the relationship between existing knowledge schemata and the processing of text is summarized in this report. The first section is concerned with dynamic processes of story understanding, with emphasis on the integration of information. The role of prior knowledge in accommodating parts of…

Improving Low-Income Preschoolers' Word and World Knowledge: The Effects of Content-Rich Instruction

ERIC Educational Resources Information Center

Neuman, Susan B.; Kaefer, Tanya; Pinkham, Ashley M.

2016-01-01

This study examined the efficacy of a shared book-reading approach to integrating literacy and science instruction. The purpose was to determine whether teaching science vocabulary using information text could improve low-income preschoolers' word knowledge, conceptual development, and content knowledge in the life sciences. Teachers in 17…

The Unified Medical Language System (UMLS): integrating biomedical terminology

PubMed Central

Bodenreider, Olivier

2004-01-01

The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP. PMID:14681409

The Unified Medical Language System (UMLS): integrating biomedical terminology.

PubMed

Bodenreider, Olivier

2004-01-01

The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900,000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.

Craniofacial embryology and postnatal development of relevant parts of the upper respiratory system.

PubMed

Halewyck, S; Louryan, S; Van Der Veken, P; Gordts, F

2012-01-01

To compare historical and current knowledge relating to the development of the paranasal sinuses, the nose and face, the Eustachian tube and temporal bones, particularly with respect to chronic inflammation during childhood. Traditional literature data, mainly emanating from text books, were supplemented with information based on a non-structured PubMed search covering the last two decades. Historical knowledge has most often been confirmed, sometimes supplemented and only rarely challenged by present-day studies. Recent studies focus mainly on the clinical application of modern imaging techniques. Interest in the development of relevant parts of the upper respiratory system remains as lively as ever. Imaging techniques with low or absent radiation exposure may give rise to a novel field of research, especially with respect to paediatric rhinosinusitis.