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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


  1. 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

  2. timoelliott.com xkcd.com

  3. 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

  4.  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

  5. xkcd.com melissa.garrido@mssm.edu @ GarridoMelissa

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