INTELLIGENT DECISION SYSTEM FOR LUNG DISEASE IN AIDS

Project Details

Description

The proposed research has the broad long-term objective of providing useful
computer-based consultative assistance to clinicians faced with complex
choices among diagnostic and therapeutic options. This advice will be
provided by an intelligent decision system, a computer program employing
artificial intelligence techniques to automate the generation of decision
models and analysis of those models to produce a recommendation. The
advantages of incorporating decision analytic principles are explicit
consideration of uncertainty and patients' preferences, and an axiomatic
approach which links the decision elements to a recommendation. The system
will permit users to control the elements of a decision model and will thus
constitute a decision analysis workbench rather than a traditional expert
system.

The project will focus on the evaluation of pulmonary infiltrates in
patients with the Acquired Immunodeficiency Syndrome (AIDS) or suspected
AIDS. This is an important and increasingly common problem which involves
high stakes for individual patients and a bewildering array of diagnostic
and therapeutic options with complex trade-offs for clinicians. In this
medical area, knowledge of disease prognoses and efficacy of therapy is
rapidly accumulating. Thus, diagnostic and therapeutic strategies must
continuously evolve in response to new data.

The system is to be implemented in the Common LISP programming language on
an Intel 80386-based microcomputer. The system will employ separate
knowledge bases for decision analytic knowledge and medical domain
knowledge. The modularity inherent in this organization will facilitate
expansion and refinement of the knowledge base in response to new research
findings and the availability of new techniques. The system will use a
frame-based representation of diseases, diagnostic tests and treatments.
These frames and their relations will determine the alternatives and
outcomes modeled by the system. A network representation of probabilistic
dependencies will ensure that the consistent updating of probabilities is
performed in each decision tree context. Generation of a decision model
will be guided by context-dependent rules which will determine at any given
point in the tree which events to consider and how deeply to expand the
decision model. The system will also contain facilities to tailor
preference functions and probabilities to individual patients. Abstracted
cases from the medical records of patients seen in our institution who have
pulmonary infiltrates and AIDS or suspected AIDS will be used for system
evaluation.
StatusFinished
Effective start/end date5/1/904/30/96

Funding

  • U.S. National Library of Medicine
  • U.S. National Library of Medicine
  • U.S. National Library of Medicine
  • U.S. National Library of Medicine

ASJC

  • Medicine(all)
  • Health Professions(all)
  • Infectious Diseases
  • Pulmonary and Respiratory Medicine

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