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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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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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