Human health care is heavily depending on the timely access to medical informtion. Since the serials/journals cover research and development news in the form of scientific articles, news items, new result of research, etc., meant for scientific community, the are proven prestigous communication vehicle amongst the scientists in the world. But, a number of surveys revealed that most relevant and frequently required medical journals are not available in most of the medical libraries in India. At present, there is no any union catalogue of medical periodicals available in India. Under the circumstances, this paper provides a conceptual plan of designing a web portal for sharing periodical holding details among medical libraries in India.

Thesis (D.L.S.)--Columbia University, 1983. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 199-207).

Thesis (Ph. D.)--Texas Woman's University, 1991. / Vita. Includes bibliographical references (leaves 80-83).

Artificial Intelligence Lab, Department of MIS, University of Arizona / Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be â medically-related.â This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or â concept space,â and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders on a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLSâ systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.

Artificial Intelligence Lab, Department of MIS, University of Arizona / In this paper we describe MedTextus, an online medical search portal with dynamic search and browse tools. To search for information, MedTextus lets users request synonyms and related terms specifically tailored to their query. A mapping algorithm dynamically builds the query context based on the UMLS ontology and then selects thesaurus terms that fit this context. Users can add these terms to their query and meta-search five medical databases. To facilitate browsing, the search results can be reviewed as a list of documents per database, as a set of folders into which all the documents are automatically categorized based on their content, and as a map that is built on the fly. We designed a user study to compare these dynamic support tools with the static query support of NLM Gateway and report on initial results for the search task. The users used NLM Gateway more effectively, but used MedTextus more efficiently and preferred its query formation tools.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user's query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts' terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical terms, especially when these terms are limited by our DSP algorithm.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individualâ s dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural networks were trained with this information to capture the relationships between the plasma tobramycin levels and the following factors: patient-related demographic factors, dosing regimens, and time of blood drawn. The data were also analyzed using a standard population pharmacokinetic modeling program, NONMEM. The observed vs predicted concentration relationships obtained from the neural network approach were similar to those from NONMEM. The residuals of the predictions from neural network analyses showed a positive correlation with that from NONMEM. Average absolute errors were 33.9 and 37.3% for neural networks and 39.9% for NONMEM. Average prediction errors were found to be 2.59 and -5.01% for neural networks and 17.7% for NONMEM. We concluded that neural networks were capable of capturing the relationships between plasma drug levels and patient-related prognostic factors from routinely collected sparse withinpatient pharmacokinetic data. Neural networks can therefore be considered to have potential to become a useful analytical tool for population pharmacokinetic data analysis.

This poster highlights a collaboration between a health sciences library and an integrative medicine program, to organize the latter's collection, offer its content via the library's online catalog, and allow limited circulation while maintaining the physical collection in the program. With limited library staff and a need to maintain availability of materials to the program, numerous issues needed addressing. First, the library did not have a cataloger with sufficient subject expertise and available time to handle the project in a timely manner. The solution was collaboration between a reference librarian with subject expertise and technical services personnel. Technical issues involved creating new locations in the online catalog and suppressing cataloging records from public view until the program is ready to share resources with the university community. NLM call numbers and Medical Subject Headings were used to achieve complete integration with the library's catalog. Some original cataloging was needed; subject headings in older records were updated (i.e., changing the old heading alternative medicine to the current complementary therapies). Some titles falling outside of health sciences fields needed Library of Congress call numbers. Future plans include: completing work on existing volumes, the library continuing to catalogthe program's materials, and setting up a circulation station in the program's library, with materials circulating according to policies determined by the program in consultation with appropriate library units. The collaboration between reference and technical services librarians has offered benefits both for the librarians involved and for the library as a whole. The integrative medicine program has not yet opened its doors to public use, so it is too early to report on feedback from users outside the program. However, the program is finding that NLM cataloging allows more efficient organization and retrieval of materials. All university departments benefit by access to a more extensive collection in this specialized area. The integrative medicine program and the library are finding this collaboration fruitful, and the programs's faculty and staff look forward to sharing resources with the university community.

This 130-page guide is a primer (including tools and resources) for planning and evaluating health information programs. It was developed by the National Network of Libraries of Medicine, Pacific Northwest Region and the National Library of Medicine.