Mitigation of outcome conflation in predicting patient outcomes using electronic health records.
Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated with false positives. This study aims to evaluate the impact of confounders on the specificity of single-outcome prediction models and assess the effectiveness of a multi-class architecture in mitigating outcome conflation.
Author(s): Reincke, S Momsen, Espinosa, Camilo, Chung, Philip, James, Tomin, Berson, Eloïse, Aghaeepour, Nima
DOI: 10.1093/jamia/ocaf033