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This course presents the main concepts of decision analysis, artificial intelligence and predictive model construction and evaluation in the specific context of medical applications.

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English

English [CC]

FREE

Description

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.

Course content

  • Introduction to Medical Decision Support Unlimited
  • Simple Probabilistic Reasoning Unlimited
  • Fuzzy & Rough Sets – Part 1 Unlimited
  • Fuzzy & Rough Sets – Part 2 Unlimited
  • Bayesian Networks – Part 1: Representation & Reasoning Unlimited
  • Bayesian Networks – Part 2: Learning From Data Unlimited
  • Logistic Regression – Part 1 Unlimited
  • Logistic Regression – Part 2 Unlimited
  • Classification Trees & CART Unlimited
  • Artificial Neural Networks Unlimited
  • Support Vector Machines Unlimited
  • Evaluation of Predictive Models – Part 1 Unlimited
  • Evaluation of Predictive Models – Part 2 Unlimited
  • Optimization and Complexity Unlimited
  • Survival Analysis Unlimited
  • Review of Predictive Methods Unlimited
  • Applied Informatics in Cardiology Unlimited
  • Review of Clustering Unlimited

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Instructor

Massachusetts Institute of Technology
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