This course will use logistic regression for clinical risk predictions Students will use the statistical package R to create risk models using an existing dataset of non diabetes patients undergoing hemoglobin A1c testing (Wells, 2018). The dataset was extracted from the Wake Forest electronic health record and is ready for analyses with minimal cleaning.
Thursdays July 1, 2021 through September 16, 2021, 6:00 - 7:00 pm
Example discussion topics include:
- Overview of medical decision making and potential uses for risk models
- Using EHR data for research
- Creation and validation of risk models, using logistic regression
- Deciding when it may be appropriate to use newer machine learning methods (e.g. random forest or deep learning)
- Variable selection
- Missing data
- Clinical implementation of prediction tools
Contact Laya Mohan firstname.lastname@example.org with any questions!