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Clinical Decision Making Group Information Director: Peter Szolovits ([email protected]) Mission: To provide better health care through applied artificial intelligence.
Clinical Decision Making
Group Information
Director:
Peter Szolovits ([email protected])
Mission:
To provide better health care through applied artificial intelligence.
Description:
The Clinical Decision Making Group at the MIT Laboratory for Computer Science is a research group dedicated to exploring and furthering the application of technology and artificial intelligence to clinical situations.
Because of the vital and crucial nature of medical practice, and the need for accurate and timely information to support clinical decisions, the group is also focused on the gathering, availability, security and use of medical information throughout the human "life cycle" and beyond.
Training Programs
Courses Photo Gallery Internal (MIT only)
Contact:
People
Phone: (617) 253-5860
Fax: (617) 258-8682
Email: [email protected]
Web: http://www.medg.lcs.mit.edu/
Projects
Guardian Angel
Personal lifelong active medical assistants
EMRS
The Electronic Medical Record System project aims to provide unified common medical record access via the world-wide web.
Heart Disease Assisting the diagnosis and therapy of cardiovascular disease. Physicians can try the diagnosis program.
Projects
MAITA
The Monitoring, Analysis, and Interpretation Tool Arsenal provides means for automated gathering, understanding, and reacting to important information in a broad range of application areas, including clinical, military, industrial, commercial, and scientific monitoring and surveillance.
Geninfer Assisting clinical genetics counselors Case-Based
Reasoning Learning diagnostic expertise from experience
Heart Disease Program
Purpose:
To assist physicians in the diagnosis of patients with cardiac symptoms, focusing
on hemodynamic dysfunction.
Description:
The Heart Disease Program is a computer system to act as an intellectual sounding board, assisting the physician in the task of differential diagnosis and anticipating the effects of therapy in the domain of cardiovascular disorders.
To address these problems we have developed two significant methodologies for medical reasoning.
For diagnosis we have responded to the challenges of this very rich domain with a diagnostic mechanism that combines probabilistic reasoning in a Bayesian network with the constraints composed by the severities of the states and the temporal relations of causality.
This allows the Heart Disease Program (HDP) to generate differential diagnoses that are consistent with respect to the known conditions of causality in the medical domain.
The hypotheses that make up the differential are causal networks representing the likely mechanisms causing and complicating the hemodynamic dysfunctions at a clinical level of detail. Most of this web focuses on the diagnostic program.
For predicting the effects of therapy we have developed a mechanism that uses equations for the hemodynamic relationships and a signal flow technique to calculate the likely quantitative steady-state change for all parameters given changes in therapies (or other parameter changes).
This mechanism effectively captures the hemodynamic effects of the therapies on which it has been tested for a variety of pathophysiologic conditions.
Questions, comments about this site?
Email: [email protected]
KR Conferences
Hosting the web site for the Principles of Knowledge Representation and Reasoning Conferences.
Questions, comments about this site?
Email: [email protected]
Guardian Angel.
Current health information systems are built for the convenience of health care providers and consequently yield fragmented patient records in which medically relevant lifelong information is sometimes incomplete, incorrect, or inaccessible.
We are constructing information systems centered on the individual patient instead of the provider, in which a set of guardian angel (GA) software agents integrates all health-related concerns, including medically-relevant legal and financial information, about an individual (its subject).
This personal system will help track, manage, and interpret the subject's health history, and offer advice to both patient and provider.
Guardian Angel
Minimally, the system will maintain comprehensive, cumulative, correct, and coherent medical records, accessible in a timely manner as the subject moves through life, work assignments, and health care providers.
Each GA is an active process that performs several important functions: it collects patient data; it checks, interprets, and explains to the subject medically-relevant facts and plans; it adapts its advice based on the subject's prior experiences and stated preferences
it performs sanity checks on both medical efficacy and cost-effectiveness of diagnostic conclusions and therapeutic plans; it monitors progress; it interfaces to software agents of providers, insurers, etc.; and it helps educate, encourage, and inform the patient.
All this serves to improve the quality of medical decision-making, increase patient compliance, and minimize iatrogenic disease and medical errors.
Heart Disease Program
Project Information Group:
Clinical Decision Making Group
Project Leader:
Bill Long ([email protected])
Purpose:
To assist physicians in the diagnosis of patients with cardiac symptoms, focusing on hemodynamic dysfunction.
Description:
The Heart Disease Program is a computer system to act as an intellectual sounding board, assisting the physician in the task of differential diagnosis and anticipating the effects of therapy in the domain of cardiovascular disorders. To address these problems we have developed two significant methodologies for medical reasoning.
For diagnosis we have responded to the challenges of this very rich domain with a diagnostic mechanism that combines probabilistic reasoning in a Bayesian network with the constraints imposed by the severities of the states and the temporal relations of causality.
This allows the Heart Disease Program (HDP) to generate differential diagnoses that are consistent with respect to the known conditions of causality in the medical domain.
The hypotheses that make up the differential are causal networks representing the likely mechanisms causing and complicating the hemodynamic dysfunctions at a clinical level of detail. Most of this web focuses on the diagnostic program.
For predicting the effects of therapy we have developed a mechanism that uses equations for the hemodynamic relationships and a signal flow technique to calculate the likely quantitative steady-state change for all parameters given changes in therapies (or other parameter changes).
This mechanism efectively captures the hemodynamic effects of the therapies on which it has been tested for a variety of pathophysiologic conditions.
Questions, comments about this site?
Email: [email protected]
Case Based Reasoning in Cardiovascular Disease
Over the last eight years, we have been working on the problem of case-based reasoning (CBR) for medical diagnosis. Through a succession of research projects, we developed a system that used physiologic causes to match findings in cases, evaluated the system on 240 cases, and developed a system that divides cases and memory based on the diagnostic units in the case.
Each of these steps has been a significant advance toward diagnostic systems that can effectively learn from experience. Still, it is clear that CBR has not reached its potential to effectively handle the case material and work in concert with a model-based program.
Documents
Phyllis A. Koton, ``Using Experience in Learning and Problem Solving,'' MIT PHD Thesis, May 1988.
David S. Aghassi, Evaluating Case-Based Reasoning for Heart Failure Diagnosis, MIT/-LCS/TR-478, (MS Thesis), June 1990.
Yeona Jang, HYDI: A Hybrid System with Feedback for Diagnosing Multiple Disorders, MIT Ph. D. Thesis, September 1993.
