title: Automated analysis of medical text. I. Clue gathering.
abstract: 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.
authors.affiliation: Gabrieli Medical Information Systems, Buffalo, NY 14202.
authors.forename: E R
authors.initials: ER
authors.lastname: Gabrieli
authors.forename: D J
authors.initials: DJ
authors.lastname: Speth
mesh_terms: D000043:Abstracting and Indexing; D001185:Artificial Intelligence; D009323:Natural Language Processing; D011643:Publishing