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PubMed1998

Toward information extraction: identifying protein names from biological papers.

To solve the mystery of the life phenomenon, we must clarify when genes are expressed and how their products interact with each other. But since the amount of continuously updated knowledge on these interactions is massive and is only available in the form of published articles, an intelligent information extraction (IE) system is needed. To extract these information directly from articles, the system must firstly identify the material names. However, medical and biological documents often include proper nouns newly made by the authors, and conventional methods based on domain specific dictionaries cannot detect such unknown words or coinages. In this study, we propose a new method of extracting material names, PROPER, using surface clue on character strings. It extracts material names in the sentence with 94.70% precision and 98.84% recall, regardless of whether it is already known or newly defined.

Information Storage and Retrieval↗

PubMed1998

Automatic record hash coding and linkage for epidemiological follow-up data confidentiality.

A protocol is proposed to allow linkage of anonymous medical information within the framework of epidemiological follow-up studies. The protocol is composed of two steps; the first concerns the irreversible transformation of identification data, using a one-way hash function which is used after spelling processing. To avoid dictionary attacks, two large random files of keys, called pads, are introduced. The second step consists in the linkage of files rendered anonymous. The weight given to each linkage field is estimated by a mixture model, the likelihood of which being maximized with the Expectation and Maximization (EM) algorithm. The performance of this method has been assessed by comparing record linkage, based on exclusive use of the automatic procedure, with a manual linkage, obtained by the Burgundy Registry of Digestive Cancers. The result of the linkage of a file of 2,847 cancers with a file of 388,614 hospitalization stays in the Dijon university hospital showed a sensitivity of 97% and a specificity of 93%.

Algorithms↗

PubMed1998

The nature of lexical knowledge.

This paper considers the nature of lexical knowledge and its role in language and information processing. The lexicon is the central component of language and plays a pivotal role in current linguistic theory [3, 4] and, increasingly, in natural language processing systems [5-7]. The lexicon embodies information about the lexical items of the language and serves as the foundation for morphologic, syntactic, and semantic processing. The differences as well as commonalities among dictionaries, thesauri, and lexicons are discussed, and distinctions between words, lexical items, and terms are drawn. Next, the scope and content of the SPECIALIST lexicon are presented, followed by a discussion of certain writing conventions that can be troublesome for text processing applications. One approach to handling orthographic and other lexical variation is discussed in a section that reports on the design and implementation of the SPECIALIST lexical programs. The paper concludes with a discussion of controlled terminologies for the medical domain. Throughout the discussion, examples are drawn from the SPECIALIST lexicon and from the other UMLS knowledge sources [8, 9].

Humans↗

PubMed1976

Subject cataloging practices in North American medical school libraries.

A survey of North American medical school libraries was made to determine current trends in subject cataloging practices. First, responses from 114 of these libraries are recorded and analyzed in the following areas: subject heading authority lists employed; use of the divided versus the dictionary catalog; and the form in which local subject authority files are kept. Then, focusing on 78 libraries that use MeSH in combination with a divided catalog, a further analysis of responses is made concerning issues relating to subject cataloging practices: updating the subject catalog to conform to annual MeSH changes; use of guide cards in the catalog; use of MeSH subheadings; filing conventions; and related issues. An attempt is made to analyze the extent to which these libraries vary from NLM practices. Suggestions are offered for formulating subject cataloging practices for an individual library. Finally, while it is concluded that MeSH and the Current Catalog are useful tools, a more detailed explication of the use of MeSH and NLM cataloging practices would be beneficial.

Cataloging↗

PubMed1998

Word analysis--a useful tool in learning the language of medicine in English.

Derivated medical terms can consist of a prefix, one or two word roots, a combining vowel, and a suffix in various combinations. To teach medical vocabulary means to teach the tools of word analysis that will enable understanding of complex terminology instead of memorising the whole medical dictionary. (Ref. 6.)

Terminology as Topic↗

PubMed1998

Validation of clinical problems using a UMLS-based semantic parser.

The capture and symbolization of data from the clinical problem list facilitates the creation of high-fidelity patient resumes for use in aggregate analysis and decision support. We report on the development of a UMLS-based semantic parser and present a preliminary evaluation of the parser in the recognition and validation of disease-related clinical problems. We randomly sampled 20% of the 26,858 unique non-dictionary clinical problems entered into OMR (Online Medical Record) between 1989 and August, 1997, and eliminated a series of qualified problem labels, e.g., history-of, to obtain a dataset of 4122 problem labels. Within this dataset, the authors identified 2810 labels (68.2%) as referring to a broad range of disease-related processes. The parser correctly recognized and validated 1398 of the 2810 disease-related labels (49.8 +/- 1.9%) and correctly excluded 1220 of 1312 non-disease-related labels (93.0 +/- 1.4%). 812 of the 1181 match failures (68.8%) were caused by terms either absent from UMLS or modifiers not accepted by the parser; 369 match failures (31.2%) were caused by labels having patterns not recognized by the parser. By enriching the UMLS lexicon with terms commonly found in provider-entered labels, it appears that performance of the parser can be significantly enhanced over a few subsequent iterations. This initial evaluation provides a foundation from which to make principled additions to the UMLS lexicon locally for use in symbolizing clinical data; further research is necessary to determine applicability to other health care settings.

Medical Records Systems, Computerized↗

PubMed1976

[Information-retrieval system for pathomorphological studies of neoplastic diseases].

The information retrieval system of studying the operative oncological material, realized on manual punch cards, is suggested. Coding and decoding of signs is accomplished by means of a descriptive dictionary. The latter comprises 18 coded signs. The method of making a list of descriptors is given. A brief technic of handling a code card is presented.

Information Systems↗

PubMed1999

Didactic content and teaching methodologies on required allopathic US family medicine clerkships.

BACKGROUND AND OBJECTIVES: Despite the increased prominence of family medicine clerkships in required third- and fourth-year clinical rotations in US allopathic medical schools, the content of these clerkships varies markedly among institutions, and there is little in the literature concerning the current or desired content of family medicine clerkships. This study explores the didactic content of a national sample of required family medicine clerkships to assess what and how this important aspect of clerkship curriculum is taught. METHODS: Using an original survey instrument, we surveyed US medical schools through mailings and follow-up phone contacts. We categorized free-form responses using a coding dictionary specific to this study and computed descriptive statistics. RESULTS: Of 127 medical schools contacted, 105 (83%) responded. Among respondents, 86 (82%) had a required family medicine clerkship, 80% of them in the third year. Mean clerkship length was 5.3 weeks (median = 4 weeks), and the mean number of didactic sessions was about 2 per week. Almost 80% of clerkships had sessions in the broad area of family medicine, and prevention was the most frequent individual topic, taught in 32 (37%) of clerkships. Seventy-one percent of sessions used methodologies other than lectures. The mean time devoted to teaching 24 of the top 26 topics identified in the survey was between 1.2 and 3.1 hours/rotation, although case presentations and common problems each averaged more than 7 hours on clerkships teaching these topics. CONCLUSIONS: This survey provided more detailed information than previously available about the didactic content of required US allopathic family medicine clerkships. The survey also documented the lack of agreement among these clerkships on didactic content. Most didactic sessions used interactive rather than lecture format. The information from this first detailed survey provides family medicine clerkship directors with national comparisons of didactic content and methodology as a foundation for further discussion.

Clinical Clerkship↗

PubMed1964