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.

Automated classification of cardiology diagnoses based on textual medical reports

“…Several works support the development of techniques like ours as relevant [Prince and Roche 2009;Gabrieli and Speth 1990;Baud et al 1992] and studies have demonstrated the need to apply techniques such as the one employed in this paper so that data can be used in an effective way [Ribeiro et al 2020;Paixao et al 2018;Hughes et al 2004]. The results achieved demonstrate not only that our work is relevant, but also that it is applicable to a wide range of scenarios.…”

Automated classification of cardiology diagnoses based on textual medical reports

“…Several works explain why the development of techniques like ours is so important [Prince and Roche 2009;Gabrieli and Speth 1990;Baud et al 1992] and studies have demonstrated the need to apply techniques such as the one employed in this paper so that data can be used in an effective way [Ribeiro et al 2020;Paixao et al 2018;Hughes et al 2004]. This demonstrates not only that our work is relevant, but also that there is a large space for it's application.…”

Evaluation of narrative text for case finding: The need for accuracy measurement

“…Sorock et al [1996] calculated the degree of sampling error that occurred with this limited categorization, but never applied the degree of error to the whole population, thereby markedly reducing estimates of pre-crash activities, crash types, and their financial impact. We suggest that investigators in this field make a shift in paradigms from case-finding to dataset analysis [Gabrieli and Speth, 1990]. When a keyword search is applied to a text field, it is analogous to performing a diagnostic screening test on a population [Jaeschke et al, 1994].…”