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Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. This course covers material from clinical epidemiology, biostatistics and machine learning as applied to retrospective research using electronic health record data.
- To understand the information gaps and problems during clinical decision making
- To become familiar with secondary use of clinical data – its potentials, pitfalls, challenges – using the Medical Information Mart for Intensive Care (MIMIC) database
- To learn the steps in parsing a clinical question into a study design and methodology for data analysis and interpretation
- To go over a collection of case studies using the MIMIC database
Students will be required to submit a project proposal by the middle of the semester and present their preliminary findings at the end of the semester. There will be exercises throughout the semester and a mid-term exam about concepts learned during the first half of the semester.
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