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HST.951J / 6.873J Medical Decision Support, Spring 2003

Comparison of logistic regression vs. neural networks as prognostic models.
Comparison of logistic regression vs. neural networks as prognostic models. (Image by Prof. Lucila Ohno-Machado.)

Highlights of this Course

In addition to sample exams, a number of lecture notes and homework assignments are available for this course.

Course Description

This course presents the main concepts of decision analysis, artificial intelligence and predictive model construction and evaluation in the specific context of medical applications. It emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Its technical focus is on decision support, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks, rough sets), and techniques to evaluate the performance of such systems. It reviews computer-based diagnosis, planning and monitoring of therapeutic interventions. It also discusses implemented medical applications and the software tools used in their construction. Students produce a final project using the machine learning methods learned in the course, based on actual clinical data.


Prof. Lucila Ohno-Machado
Prof. Isaac Kohane
Prof. Peter Szolovits
Prof. Staal Vinterbo

Guest Lecturers:
Prof. Stephan Dreiseitl
Prof. Ju Jan Kim
Prof. Bill Long
Prof. Marco Ramoni
Prof. Fred Resnic
Prof. David Wypij

Course Meeting Times

One session / week
1.5 hours / session



Additional Features

   Chinese (Simplified)


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