GIS: a biomedical text-mining system for gene information discovery.
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We present a biomedical text-mining system focused on four types of gene-related information: biological functions, associated diseases, related genes and gene-gene relations. The aim of this system is to provide researchers an easy-to-use bio-information service that will rapidly survey the rapidly burgeoning biomedical literature. AVAILABILITY: http://iir.csie.ncku.edu.tw/~yuhc/gis/ (+info)
Development and validation of the CAM Health Belief Questionnaire (CHBQ) and CAM use and attitudes amongst medical students.
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BACKGROUND: The need for Complementary and Alternative Medicine (CAM) and holistic approaches in allopathic medical school curricula has been well articulated. Despite increased CAM instruction, feasible and validated instruments for measuring learner outcomes in this content area do not widely exist. In addition, baseline attitudes or beliefs of medical students towards CAM, and the factors that may have formed them, including use of CAM itself, remain unreported. METHODS: A 10-item measure (CHBQ--CAM Health Belief Questionnaire) was constructed and administered to three successive classes of medical students simultaneously with the previously validated 29-item Integrative Medicine Attitude Questionnaire (IMAQ). Both measures were imbedded in a baseline needs assessment questionnaire. Demographic and other data were collected on students' use of CAM modalities and their awareness and use of primary CAM information resources. Analysis of CHBQ items was performed and its reliability and criterion-related validity were established. RESULTS: Response rate was 96.5% (272 of 282 students studied). The shorter CHBQ compared favorably with the longer IMAQ in internal consistency reliability. Cronbach's coefficient alpha was 0.75 and 0.83 for the CHBQ and IMAQ respectively. Students showed positive attitudes/beliefs towards CAM and high levels of self-reported CAM use. The majority (73.5%) of students reported using at least one CAM modality, and 54% reported using at least two modalities. Eighty-one percent use the internet as a primary source of information for CAM. CONCLUSIONS: The CHBQ is a practical, valid and reliable instrument for measuring medical student attitudes/beliefs and has potential utility for measuring the impact of CAM instruction. Medical students showed a high self-reported rate of CAM use and positive attitudes towards CAM. Short, didactic exposure to CAM instruction in the first year of medical school did not additionally impact these already positive attitudes. Unlike the IMAQ, which was intended for use with physicians, the CHBQ is generic in design and content and applicable to a variety of learner types. Evaluation measures must be appropriate for specific CAM instructional outcomes. (+info)
Text categorization models for retrieval of high quality articles in internal medicine.
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The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [1]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power. (+info)
The KnowledgeMap project: development of a concept-based medical school curriculum database.
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We developed the KnowledgeMap (KM) system as an online, concept-based database of medical school curriculum documents. It uses the KM concept indexer to map full-text documents and match search queries to concepts in the Unified Medical Language System (UMLS). In this paper, we describe the design of KM and report the first seven months of its implementation into a medical school. Despite being emphasized in only two first year courses and one fourth year course, students from all four classes used KM to search and browse documents. All faculty members involved with courses piloting KM used the system to upload and manage lecture documents. Currently, we are working with eight course directors to transition their courses to KM for next year. (+info)
Gene indexing: characterization and analysis of NLM's GeneRIFs.
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We present an initial analysis of the National Library of Medicine's (NLM) Gene Indexing initiative. Gene Indexing occurs at the time of indexing for all 4600 journals and over 500,000 articles added to PubMed/MEDLINE each year. Gene Indexing links articles about the basic biology of a gene or protein within eight model organisms to a specific record in the NLM's LocusLink database of gene products. The result is an entry called a Gene Reference Into Function (GeneRIF) within the LocusLink database. We analyzed the numbers of GeneRIFs produced in the first year of GeneRIF production. 27,645 GeneRIFs were produced, pertaining to 9126 loci over eight model organisms. 60% of these were associated with human genes and 27% with mouse genes. About 80% discuss genes with an established MeSH Heading or other MeSH term. We developed a prototype functional alerting system for researchers based on the GeneRIFs, and a strategy to find all of the literature related to genes. We conclude that the Gene Indexing initiative adds considerable value to the life sciences research community. (+info)
Creating knowledgebases to text-mine PUBMED articles using clustering techniques.
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Knowledgebase-mediated text-mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing (+info)
Development of infobuttons in a wireless environment.
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At Columbia-Presbyterian Medical Center, a clinical information system (CIS) called WebCIS is used by health care providers. We have developed a palm-based extension to WebCIS, called PalmCIS, which provides physicians access to clinical data through a wireless connection. As part of PalmCIS, we have added links to two on-line information resources: PubMed and Micromedex. (+info)
Visual mapping for medical concepts.
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Concept relationships are traditionally defined in human-generated vocabulary lists such as the Medical Subject Headings (MeSH). This poster describes a prototype system that automatically generates concept relationships from the medical literature. The system is directly connected to the PUBMED search engine. For any given medical concept, the system will generate two styles of visual maps from MEDLINE in real time. Users can use the maps to explore concept relationships or construct better search queries interactively. (+info)