Learning Objectives Recognize parametric and nonparametric methods for predicting patient outcomes from longitudinal data List different types of patient data that may be used to predict hospital readmission and disease-free survival Discuss at least two patient outcomes that can be predicted from longitudinal data
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Communicating Predicted Risks What is the goal of the model? How certain are we of the predictions? Individual or group risk? Translation: Consider numeracy, prospect theory, risk framing
Bruno Riverin The Effect of Timely Outpatient Follow-up on 30-Day Readmission: An Analysis Using Time-Specific Propensity Scores Brian Moore Comorbidity Indices for Identifying Risk of Readmission or In- Hospital Mortality Using Hospital Administrative Data Che Ngufor Predicting Change in Glycemic Control among Previously Controlled Type 2 Diabetes Patients Using Longitudinal Data: A Machine Learning Approach Layla Parast Improving Dynamic Risk Prediction Using Biomarker Change Measurements
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