THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision Making

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Citation: Harold P. Lehmann and Edward H. Shortliffe. (1990) THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision Making. In KSL-90-10, 1990.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Harold P. Lehmann and Edward H. Shortliffe
title THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision Making
number KSL-90-10
institution Knowledge Systems, AI Laboratory
address Washington D.C.
year 1990
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abstract Previous knowledge-based systems for statistical analysis separate the numeric knowledge in the data analysis from the heuristic knowledge in using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert systems allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds, (2) to update a statistical model on the basis of data from the study and the userUs prior beliefs, and (3) to construct those models dynamically. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge, including methodological knowledge. Constructing such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The user of such a system can interact with the system at the level of general methodological concerns, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician reader to interpret the results of a published randomized clinical trial for clinical decision making.

KSL Technical Report ID: KSL-90-10
Facts about THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision MakingRDF feed
Abstract Previous knowledge-based systems for stati … Previous knowledge-based systems for statistical analysis separate the numeric knowledge in the data analysis from the heuristic knowledge in using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert systems allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds, (2) to update a statistical model on the basis of data from the study and the userUs prior beliefs, and (3) to construct those models dynamically. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge, including methodological knowledge. Constructing such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The user of such a system can interact with the system at the level of general methodological concerns, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician reader to interpret the results of a published randomized clinical trial for clinical decision making. inical trial for clinical decision making.
Address Washington D.C.  +
Author Harold P. Lehmann and Edward H. Shortliffe  +
Bibtype techreport  +
Has author Harold P. Lehmann and Edward H. Shortliffe  +
Has identifier KSL-90-10  +
Has publishing details 1990  +
Has title THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision Making  +
Has where published KSL-90-10  +
Has year 1990  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-90-10  +
Number KSL-90-10  +
Process note YES  +
Title THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision Making  +
Year 1990  +
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