Medical Informatics and Clinical Decision Making: The Science and the Pragmatics

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Citation: Edward H. Shortliffe. (1991) Medical Informatics and Clinical Decision Making: The Science and the Pragmatics. In KSL-91-50, 1991.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Edward H. Shortliffe
title Medical Informatics and Clinical Decision Making: The Science and the Pragmatics
number KSL-91-50
institution Knowledge Systems, AI Laboratory
year 1991
Bibtex more
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abstract There are important scientific and pragmatics synergies between the medical decision making field and the emerging discipline known as medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classical probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, I describe recent work in our laboratory which has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. I illustrate this point with an example drawn from outcomes management research.

KSL Technical Report ID: KSL-91-50
Facts about Medical Informatics and Clinical Decision Making: The Science and the PragmaticsRDF feed
Abstract There are important scientific and pragmat … There are important scientific and pragmatics synergies between the medical decision making field and the emerging discipline known as medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classical probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, I describe recent work in our laboratory which has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. I illustrate this point with an example drawn from outcomes management research. e drawn from outcomes management research.
Author Edward H. Shortliffe  +
Bibtype techreport  +
Has author Edward H. Shortliffe  +
Has identifier KSL-91-50  +
Has publishing details 1991  +
Has title Medical Informatics and Clinical Decision Making: The Science and the Pragmatics  +
Has where published KSL-91-50  +
Has year 1991  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-91-50  +
Number KSL-91-50  +
Process note YES  +
Title Medical Informatics and Clinical Decision Making: The Science and the Pragmatics  +
Year 1991  +
Retrieved from "http://tw.rpi.edu/wiki/Medical_Informatics_and_Clinical_Decision_Making:_The_Science_and_the_Pragmatics"
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