Medical Decision Support
Lecture Notes
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LEC # |
TOPICS |
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1 |
Introduction to Medical Decision Support (PDF - 7.2 MB) |
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2 |
Simple Probabilistic Reasoning (PDF) |
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3 |
Fuzzy & Rough Sets - Part 1 (PDF) |
|
4 |
Fuzzy & Rough Sets - Part 2 (PDF) |
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5 |
Bayesian Networks - Part 1: Representation & Reasoning (PDF) |
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6 |
Bayesian Networks - Part 2: Learning From Data (PDF) |
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7 |
Logistic Regression - Part 1 (PDF - 6 MB) |
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8 |
Logistic Regression - Part 2 (PDF - 6 MB) |
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9 |
Unsupervised Learning |
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10 |
Classification Trees & CART (PDF) |
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11 |
Artificial Neural Networks (PDF - 2.1 MB) |
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12 |
Support Vector Machines (PDF - 1.2 MB) |
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13 |
Evaluation of Predictive Models - Part 1 (PDF) |
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14 |
Evaluation of Predictive Models - Part 2 (PDF) |
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15 |
Optimization and Complexity (PDF) |
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16 |
Survival Analysis (PDF) |
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17 |
Review of Predictive Methods (PDF) |
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Midterm Exam |
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18 |
Review of Complexity |
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19 |
Applied Informatics in Cardiology (PDF) |
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20 |
Review of Clustering (PDF) |
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21 |
Student Presentations |
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22 |
Student Presentations |
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23 |
Student Presentations |
Assignments
Homeworks are due at the end of each module. They may require programming, and all code must be included. They are to be solved individually and, if received after the deadline, may be subject to substantial grade penalty. No homeworks will be accepted after the solutions have been handed out. They are worth 30% of the course grade.
Homework 1 (PDF)
Homework 2 (PDF)
Exams
The midterm contains topics from decision analysis and machine learning. There will be a strict time limit of 1.5 h. Students are allowed to bring class notes, homework solutions, and readings to the exam. The midterm is worth 30% of the course grade.
Sample Midterm (PDF)
Sample Midterm Solutions (PDF)