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Expert systems in psychiatry. A review.
R A Morelli1, J D Bronzino, J W Goethe
1Trinity College Institute of Living, Hartford, Connecticut 06106.
Journal of Medical Systems
|June 1, 1987
Summary
This review examines computer-based decision aids for psychiatric diagnosis and consultation, focusing on five key decision-making models. It assesses the potential for expert systems in mental health, highlighting strengths and weaknesses of each approach.
Area of Science:
- Computer Science
- Psychiatry
- Artificial Intelligence
Background:
- Computer-based decision aids are increasingly used in healthcare.
- The mental health field has seen limited adoption of these technologies.
- Understanding decision-making models is crucial for developing effective systems.
Purpose of the Study:
- To review existing computer-based decision aids in psychiatric diagnosis and consultation.
- To discuss the prospects for expert system development in mental health.
- To analyze various decision-making models used in these systems.
Main Methods:
- Review of existing literature on computer-based decision aids in mental health.
- Analysis of five distinct decision-making paradigms: data bank analysis, statistical pattern recognition, Bayesian analysis, logical flow chart method, and knowledge-based (expert system) approaches.
- Evaluation of the essential features, strengths, weaknesses, and example applications of each paradigm.
Main Results:
- Identified five primary decision-making models used in computer-aided psychiatric diagnosis.
- Detailed the characteristics, advantages, and limitations of each model.
- Provided examples of their application within the mental health domain.
Conclusions:
- Expert systems hold significant promise for advancing mental health decision support.
- The choice of decision-making model impacts the system's effectiveness and applicability.
- Further research and development are needed to optimize these systems for clinical use.