Spring Term 2005
|Instructors:||Peter Szolovits, email@example.com, 32-254, (617) 253-3476,
Lucila Ohno-Machado, firstname.lastname@example.org, (617) 732-8543
|Secretary:||Fern DeOliveira, email@example.com, 32-250, (617) 253-5860.|
Meets: 6.034 lectures, recitations and tutorials, plus Wednesdays 10-11am (right before the Wednesday 6.034 lecture) in 32-250.
An intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection and monitoring. The class meets with lectures and recitations of 6.034, whose material is supplemented by additional readings and discussion sessions. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers. This class is available for credit only to graduate students in HST. It carries 12 units (5-3-4) of H-LEVEL Graduate Credit.
At each week's meeting of HST947, we will discuss a topic in the medical applications of AI, based on papers that are to have been read before the Wednesday meeting of that class. We will attempt to have the papers available on-line at this site, usually as Adobe PDF files. (Click on the Adobe logo to download a copy of the free reader for PDF.) Those papers whose PDF files contain simply a scanned image of the original are, unfortunately, rather large. You should probably only download them via a high-speed internet connection. Papers to which we do not hold copyright are in a special subdirectory that will ask you to authenticate your belonging to this class before allowing you access. The user name and password were announced at the first class meeting.
Those students who have not yet joined the HST947 mailing list for this term, please visit
to join. The class web site (this page) is available at http://courses.csail.mit.edu/HST947/.
Note: You should complete the readings for a class before the corresponding class session. The schedule below is evolving, and will change as we move through the semester and try to align HST947 readings with 6.034 material.
|Feb. 2, 2002||Organizational Meeting|
|Feb. 9||Introduction to Diagnostic Reasoning||
P. Szolovits and S. G. Pauker. Categorical and probabilistic reasoning in medical diagnosis. Artificial Intelligence, 11:115-144, 1978. (Also in PDF.)
Pauker, S. G., G. A. Gorry, J. P. Kassirer, and W. B. Schwartz (1976). "Toward the Simulation of Clinical Cognition: Taking the Present Illness." American Journal of Medicine 60:1-18.
|Feb. 16||Diagnosis by Pattern Matching and Search||
Wu, T. Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach. AAAI 1990 : 357-364
Pople, H. E., Jr. (1982). Heuristic Methods for Imposing Structure on Ill-Structured Problems: The Structuring of Medical Diagnostics. Artificial Intelligence in Medicine. P. Szolovits. Boulder, Colorado, Westview Press: 119-190.
|Feb 23||Causal Reasoning||
W. B. Schwartz, R. S. Patil, and P. Szolovits. Artificial intelligence in medicine: where do we stand. New England Journal of Medicine, 316:685-688, 1987.
R. S. Patil, P. Szolovits, and W. B. Schwartz. Causal understanding of patient illness in medical diagnosis. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence, pages 893-899, 1981.
R. S. Patil, P. Szolovits, and W. B. Schwartz. Information acquisition in diagnosis. In Proceedings of the National Conference on Artificial Intelligence, pages 345-348, American Association for Artificial Intelligence, 1982.
A copy of Patil's thesis, which forms the basis of the last two papers, is also available on-line for anyone interested in the full details.
|Mar. 2||Receiver-Operator Characteristics Curves to Evaluate Systems||T. A. Lasko, J. G. Bhagwat, K. H. Zou, L. Ohno-Machado. The Use of Receiver Operating Characteristic Curves in Biomedical Informatics, in press.|
|Mar. 9||Prognostic Modeling||G. F. Cooper, et al. Predicting Dire Outcomes of Patients with Community Acquired Pneumonia.|
|Mar. 16||Classification Methods for Gene Expression Data||
A. Statnikov, et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics Vol. 21 no. 5 2005, pages 631–643.
(Optional--you probably already know this material:)
|Mar. 23||Spring Break|
|Mar. 30||Predictive Models||
J. P. Marcin, et al. Combining physician’s subjective and physiology-based objective mortality risk predictions. Crit Care Med 2000 Vol. 28, No. 8 (2000) 2984-90.
E. W. Steyerberg, et al. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Statist. Med. 2004; 23:2567–2586.
|Apr. 6||Learning Medical Reasoning||
S Coderre, et al. Diagnostic reasoning strategies and diagnostic success. Medical Education 2003;37:695–703.
J. F. Arocha, et al. Identifying reasoning strategies in medical decision making: A methodological guide. Journal of Biomedical Informatics xxx (2005, in press).
D. L. Hunt, et al. Effects of Computer-Based Clinical Decision Support Systems on Physician Performance and Patient Outcomes. JAMA 280(15) 1339-46. (1998)
K. Kawamoto, et al. BMJ, Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. doi:10.1136/bmj.38398.500764.8F (published 14 March 2005)
E. Ammenwerth and N. de Keizer. An Inventory of Evaluation Studies of Information Technology in Health Care. Methods Inf. Med. 2005:44:44-56.
J. Nielsen. Medical Usability: How to Kill Patients Through Bad Design. http://www.useit.com/alertbox/20050411.html.
|Apr. 20||Medical Knowledge Representation||
A. L. Rector, et al. The GRAIL Concept Modelling Language for Medical Terminology. Extended version of the paper in Artificial Intelligence in Medicine 9:139-171 (1997).
B. Trombert-Paviot, et al. GALEN: a third generation terminology tool to support a multipurpose national coding system for surgical procedures. International Journal of Medical Informatics 58–59 (2000) 71–85.
|Apr. 27||Evaluation of Complex Decision Support Systems||
E. S. Berner, et al. Performance of Four Computer-Based Diagnostic Systems. New England Journal of Medicine 300:1792-6, 1994.
H. S. F. Fraser, et al. Evaluation of a Cardiac Diagnostic Program in a Typical Clinical Setting. Journal of the American Medical Informatics Association. 10:373–381, 2003.
|May 4||Rule-Based Expert Systems||
R. Davis, B. G. Buchanan and E. H. Shortliffe (1977). "Production Rules as a Representation for a Knowledge-Based Consultation Program." Artificial Intelligence 8: 15-45.
V. L. Yu, B. G. Buchanan, E. H. Shortliffe, S. M. Wraith, R. Davis, A. C. Scott and S. N. Cohen. Evaluating the Performance of a Computer-Based Consultant. Comp. Programs in Biomedicine 9(1979) 95-102.
V. L. Yu, et al. Antimicrobial Selection by a Computer: A Blinded Evaluation by Infectious Diseases Experts. JAMA 242(12): 1279-82 (1979).
|May 11||Student Presentations|