Artificial Intelligence - The INTERNIST-I And QMR Expert Systems.


INTERNIST-I and QMR (Quick Medical Reference) are two similar expert systems created in the 1970s at the University of Pittsburgh School of Medicine.

The INTERNIST-I system was created by Jack D. Myers, who worked with the university's Intelligent Systems Program head Randolph A.

Miller, artificial intelligence pioneer Harry Pople, and infectious disease specialist Victor Yu to encode his internal medicine knowledge.

The expert system's microcomputer version is known as QMR.

It was created in the 1980s by Fred E. Masarie, Jr., Randolph A. Miller, and Jack D. Myers at the University of Pittsburgh School of Medicine's Section of Medical Informatics.

The two expert systems shared algorithms and are referred to as INTERNIST-I/QMR combined.

QMR may be used as a decision support tool, but it can also be used to evaluate physician opinions and recommend laboratory testing.

QMR may also be used as a teaching tool since it includes case scenarios.

INTERNIST-I was created in a medical school course presented at the University of Pittsburgh by Myers, Miller, Pople, and Yu.

The Logic of Problem-Solving in Clinical Diagnosis requires fourth-year students to integrate lab oratory and sign-and-symptom data obtained from published and unpublished clinical icopathological reports and patient histories into the course.

The technology was also used to test the registered pupils as a "quizmaster." The team developed a ranking algorithm, partitioning algorithm, exclusion functions, and other heuristic rules instead of using statistical artificial intelligence approaches.

The algorithm generated a prioritized list of likely diagnoses based on the submitted physician findings, as well as responses to follow-up questions.

INTERNIST-I may potentially suggest further lab testing.

By 1982, the project's directors believed that fifteen person-years had been invested on the system.

The system finally included taxonomy information about 1,000 disorders and three-quarters of all known internal medicine diagnoses, making it very knowledge-intensive.

At the pinnacle of the Greek oracle approach to medical artificial intelligence, the University of Pittsburgh School of Medicine produced INTERNIST-I.

The initial generation of the system's user was mostly considered as a passive spectator.

The system's creators hoped that it might take the role of doctors in locations where they were rare, such as manned space missions, rural communities, and nuclear submarines.

The technology, on the other hand, was time-consuming and difficult to use for paramedics and medical personnel.

Donald McCracken and Robert Akscyn of neighboring Carnegie Mellon University created INTERNIST-I in ZOG, an early knowledge management hypertext system, to address this challenge.

QMR enhanced INTERNIST-user-friendliness I's while promoting more active investigation of the case study knowledge set.

QMR also used a weighted scales and a ranking algorithm to analyze a patient's signs and symptoms and relate them to diagnosis.

By researching the literature in the topic, system designers were able to assess the evocative intensity and frequency (or sensitivity) of case results.

The foundation of QMR is a heuristic algorithm that assesses evocative strength and frequency and assigns a numerical value to them.

In the solution of diagnostic problems, QMR adds rules that enable the system to convey time-sensitive reasoning.

The capacity to create homologies between several related groups of symptoms was one feature of QMR that was not available in INTERNIST-I.

QMR included not just diagnoses that were probable, but also illnesses with comparable histories, signs and symptoms, and early laboratory findings.

By comparing QMR's output with case files published in The New England Journal of Medicine, the system's accuracy was tested on a regular basis.

QMR, which was commercially offered to doctors from First DataBank in the 1980s and 1990s, needed roughly 10 hours of basic training.

Typical runs of the software on individual patient situations were done after hours in private clinics.

Instead of being a clinical decisionmaker, QMR's architects recast the expert system as a hyperlinked electronic textbook.

The National Library of Medicine, the NIH Division of Research Resources, and the CAMDAT Foundation all provided funding for INTERNIST-I/QMR.

DXplain, Meditel, and Iliad were three medical artificial intelligence decision aids that were equivalent at the time.

G. Octo Barnett and Stephen Pauker of the Massachusetts General Hospital/Harvard Medical School Laboratory of Computer Science created DXplain with funding help from the American Medical Association.

DXplain's knowledge base was derived from the American Medical Association's (AMA) book Current Medical Information and Terminology (CMIT), which described the causes, symptoms, and test results for over 3,000 disorders.

The diagnostic algorithm at the core of DXplain, like that of INTERNIST-I, used a scoring or ranking procedure as well as modified Bayesian conditional probability computations.

In the 1990s, DXplain became accessible on diskette for PC users.

Meditel was developed from an earlier computerized decision aid, the Meditel Pediatric System, by Albert Einstein Medical Center educator Herbert Waxman and physician William Worley of the University of Pennsylvania Department of Medicine in the mid-1970s.

Using Bayesian statistics and heuristic decision principles, Meditel aided in suggesting probable diagnosis.

Meditel was marketed as a doc-in-a-box software package for IBM personal computers in the 1980s by Elsevier Science Publishing Company.

In the Knowledge Engineering Center of the Department of Medical Informatics at the University of Utah, Dr.

Homer Warner and his partners nurtured Iliad, a third medical AI competitor.

The federal government awarded Applied Medical Informatics a two-million-dollar grant in the early 1990s to integrate Iliad's diagnostic software directly to computerized databases of patient data.

Iliad's core target was doctors and medical students, but in 1994, the business produced Medical HouseCall, a consumer version of Iliad. 

Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Clinical Decision Support Systems; Computer-Assisted Diagnosis.

Further Reading:

Bankowitz, Richard A. 1994. The Effectiveness of QMR in Medical Decision Support: Executive Summary and Final Report. Springfield, VA: U.S. Department of Commerce, National Technical Information Service.

Freiherr, Gregory. 1979. The Seeds of Artificial Intelligence: SUMEX-AIM. NIH Publication 80-2071. Washington, DC: National Institutes of Health, Division of Research Resources.

Lemaire, Jane B., Jeffrey P. Schaefer, Lee Ann Martin, Peter Faris, Martha D. Ainslie, and Russell D. Hull. 1999. “Effectiveness of the Quick Medical Reference as a Diagnostic Tool.” Canadian Medical Association Journal 161, no. 6 (September 21): 725–28.

Miller, Randolph A., and Fred E. Masarie, Jr. 1990. “The Demise of the Greek Oracle Model for Medical Diagnosis Systems.” Methods of Information in Medicine 29, no. 1: 1–2.

Miller, Randolph A., Fred E. Masarie, Jr., and Jack D. Myers. 1986. “Quick Medical Reference (QMR) for Diagnostic Assistance.” MD Computing 3, no. 5: 34–48.

Miller, Randolph A., Harry E. Pople, Jr., and Jack D. Myers. 1982. “INTERNIST-1: An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine.” New England Journal of Medicine 307, no. 8: 468–76.

Myers, Jack D. 1990. “The Background of INTERNIST-I and QMR.” In A History of Medical Informatics, edited by Bruce I. Blum and Karen Duncan, 427–33. New York: ACM Press.

Myers, Jack D., Harry E. Pople, Jr., and Jack D. Myers. 1982. “INTERNIST: Can Artificial Intelligence Help?” In Clinical Decisions and Laboratory Use, edited by Donald P. Connelly, Ellis S. Benson, M. Desmond Burke, and Douglas Fenderson, 251–69. Minneapolis: University of Minnesota Press.

Pople, Harry E., Jr. 1976. “Presentation of the INTERNIST System.” In Proceedings of the AIM Workshop. New Brunswick, NJ: Rutgers University.

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