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Abstract: Most disease-outcome association analyses use approaches that use raw data from electronic health records or genomic data. We propose to augment these approaches with measure-based features developed using the canonical knowledge from known quality factors and clinical guidelines to improve the predictive power of association studies for predicting disease outcomes. Our preliminary results indicate that we can successfully utilize quality measures for predicting clinically important outcomes for patients with Acute Myocardial Infarction.

Learning Objective 1: Apply knowledge from known quality factors and clinical guidelines to improve the predictive power of association studies for predicting disease outcomes.

Authors:

John Bisognano (Presenter)
Washington University in St. Louis Medical School

Aditi Gupta, Washington University in St. Louis Medical School
Matthew Lui, Washington University in St. Louis Medical School
Albert Lai, Washington University in St. Louis Medical School
Philip Payne, Washington University in St. Louis Medical School

Presentation Materials:

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