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Biomedical subjects

E R Gabrieli

Publications and source records attributed to E R Gabrieli.

At least 19 recordsLinked to original sources
PubMed1993

Computer-based medical terminology and knowledge representation.

This paper briefly summarizes the characteristics of a computer-based medical terminology constructed by the author. Its size is around 140,000, systematized by meaning using a single attribute as key: similarity among instances covered by the term. This systematization resulted in a single hierarchical tree. Initial term typing was based on the knowledge patterns of the terms, represented by the arcs (relationships) among terms (nodes). Six different types of medical terms could be identified. For coding of a medical term, its position on the hierarchical tree was used, the semantic address. It is recommended that the semantic address should be used for worldwide compatibility.

Artificial Intelligence↗
PubMed1992

Data security.

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Computer Security↗
PubMed1991

Automated analysis of medical text. II. Cognitive strategy.

In a previous paper, the authors described three paradigms applicable to automated medical text analysis. In this paper, the relative importance of the three paradigms is discussed, viz. the relative value of the linguistic word categorization, the semantic paradigm, and the medical fact delineation. The strategy adopted was to limit the linguistic disambiguation and apply probabilistic rules, in order to speed up the analytic process.

Algorithms↗
PubMed1991

Need for standards in medical communication.

Recent progress in clinical informatics resulted in two new tools: a comprehensive medical nomenclature and a prototype medical text processor. The direction of further progress is apparent. The technology is ready for large-scale computerization of health care documentation. The new progress-limiting factors seem to be related to the reorientation of the record writers and the entire health care industry. The true challenge of the 1990s is to make clinical data readily available, without jeopardizing the cherished values of medical data confidentiality and provider privacy.

Disease↗
PubMed1990

Automated analysis of medical text. I. Clue gathering.

Clinical practice of medicine is highly information-intensive. At the bedside, past experience is the primary justification of reasoning and decisions. This past medical experience is an amalgamation of textbook information and personal experience. During the last 2-3 decades, both of these major sources of clinical information have appeared less and less effective. The pace of progress, resulting in better diagnostic tools and new therapies, has undermined our personal experience, and for the same reason, the time lapse between drafting the manuscripts and distributing the textbooks has become a growing problem. Emphasis has shifted from textbooks to scientific journals with shorter publishing delays, and the role of daily newspapers and television programs seems to be growing. The traditional ways of gathering clinical knowledge and experience seem to fail more and more. In addition to textbooks and scientific journals, current clinical experience is described in millions of patient records, stored in hospitals and ambulatory care offices. However, we have no easy access to patient charts, and we are lacking a method for cost-effective merging of clinical case histories to make them suitable for much-needed statistical inferences. Computers could make a major contribution in this area, but first we must bridge the gap between the narrative text in the medical record and computer technology. Recently, much encouraging progress has been made in automated medical text processing, the topic of this paper.

Abstracting and Indexing↗