In this paper we describe an approach to respond to a request for information with the identification and location of the appropriate person as a source of information for answering the question. The expertise of a person is characterized using a weighted profile that has been derived from a series of documents describing the expert's activities. Having these profiles, requests for information can be matched with these profiles. The best matches correspond with the people that are experts for providing information on the request. (+info)
Pathfinder: multiresolution region-based searching of pathology images using IRM.
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The fast growth of digitized pathology slides has created great challenges in research on image database retrieval. The prevalent retrieval technique involves human-supplied text annotations to describe slide contents. These pathology images typically have very high resolution, making it difficult to search based on image content. In this paper, we present Pathfinder, an efficient multiresolution region-based searching system for high-resolution pathology image libraries. The system uses wavelets and the IRM (Integrated Region Matching) distance. Experiments with a database of 70,000 pathology image fragments have demonstrated high retrieval accuracy and high speed. The algorithm can be combined with our previously developed wavelet-based progressive pathology image transmission and browsing algorithm and is expandable for medical image databases. (+info)
UMLS concept indexing for production databases: a feasibility study.
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OBJECTIVES: To explore the feasibility of using the National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus as the basis for a computational strategy to identify concepts in medical narrative text preparatory to indexing. To quantitatively evaluate this strategy in terms of true positives, false positives (spuriously identified concepts) and false negatives (concepts missed by the identification process). METHODS: Using the 1999 UMLS Metathesaurus, the authors processed a training set of 100 documents (50 discharge summaries, 50 surgical notes) with a concept-identification program, whose output was manually analyzed. They flagged concepts that were erroneously identified and added new concepts that were not identified by the program, recording the reason for failure in such cases. After several refinements to both their algorithm and the UMLS subset on which it operated, they deployed the program on a test set of 24 documents (12 of each kind). RESULTS: Of 8,745 matches in the training set, 7,227 (82.6 percent ) were true positives, whereas of 1,701 matches in the test set, 1, 298 (76.3 percent) were true positives. Matches other than true positive indicated potential problems in production-mode concept indexing. Examples of causes of problems were redundant concepts in the UMLS, homonyms, acronyms, abbreviations and elisions, concepts that were missing from the UMLS, proper names, and spelling errors. CONCLUSIONS: The error rate was too high for concept indexing to be the only production-mode means of preprocessing medical narrative. Considerable curation needs to be performed to define a UMLS subset that is suitable for concept matching. (+info)
Indexing and filing of pathological illustrations.
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An inexpensive feature card retrieval system has been combined with the Systematised Nomenclature of Pathology (SNOP) to provide simple but efficient means of indexing and filing 2 in. x 2 in. transparencies within a department of pathology. Using this system 2400 transparencies and the associated index cards can be conveniently stored in one drawer of a standard filing cabinet. (+info)
Use of keyword hierarchies to interpret gene expression patterns.
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MOTIVATION: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. RESULTS: We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes. (+info)
BioMolQuest: integrated database-based retrieval of protein structural and functional information.
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MOTIVATION: Information about a particular protein or protein family is usually distributed among multiple databases and often in more than one entry in each database. Retrieval and organization of this information can be a laborious task. This task is complicated even further by the existence of alternative terms for the same concept. RESULTS: The PDB, SWISS-PROT, ENZYME, and CATH databases have been imported into a combined relational database, BIOMOLQUEST: A powerful search engine has been built using this database as a back end. The search engine achieves significant improvements in query performance by automatically utilizing cross-references between the legacy databases. The results of the queries are presented in an organized, hierarchical way. (+info)
Medical subject headings used to search the biomedical literature.
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The National Library of Medicine's MEDLINE (MEDLARS Online) database was the first database to be searched nationwide via value-added telecommunication networks. Now available on the World Wide Web free of charge from the National Library of Medicine and from many other sources, it is the world's most heavily used medical database. MEDLINE is unique in that each reference to the medical literature is indexed under a controlled vocabulary called Medical Subject Headings (MeSH). These headings are the keys that unlock the medical literature. MeSH multiplies the usefulness of the MEDLINE database and makes it possible to search the medical literature as we do today. This paper commemorates the 40th anniversary of the introduction of MeSH and salutes some of the farsighted persons who conceived and developed the MEDLINE database. (+info)
Derivation and evaluation of a document-naming nomenclature.
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OBJECTIVE: The Computerized Patient Record System is deployed at all 173 Veterans Affairs (VA) medical centers. Providers access clinical notes in the system from a note title menu. Following its implementation at the Nashville VA Medical Center, users expressed dissatisfaction with the time required find notes among hundreds of irregularly structured titles. The authors' objective was to develop a document-naming nomenclature (DNN) that creates informative, structured note titles that improve information access. DESIGN: One thousand ninety-four unique note titles from two VA medical centers were reviewed. A note-naming nomenclature and compositional syntax were derived. Compositional order was determined by user preference survey. MEASUREMENTS: The DNN was evaluated by modeling note titles from the Salt Lake City VA Medical Center (n=877), Vanderbilt University Medical Center (n=554), and the Mayo Clinic (n=42). A preliminary usability evaluation was conducted on a structured title display and sorting application. RESULTS: Classes of note title components were found by inspection. Components describe characteristics of the author, the health care event, and the organizational unit providing care. Terms were taken from VA medical center information systems and national standards. The DNN model accurately described 97 to 99 percent of note titles from the test sites. The DNN term coverage varied, depending on component and site. Users found the DNN title format useful and the DNN-based title sorting and note review application easy to learn and quick to use. CONCLUSION: The DNN accurately models note titles at five medical centers. Preliminary usability data indicate that DNN integration with title parsing and sorting software enhances information access. (+info)