Mitigation of outcome conflation in predicting patient outcomes using electronic health records.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 40056434

Abstract

OBJECTIVES: 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.

Authors

  • S Momsen Reincke
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305, United States.
  • Camilo Espinosa
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Philip Chung
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA.
  • Tomin James
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305, United States.
  • Eloise Berson
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Nima Aghaeepour
    Departments of Anesthesiology, Pain, and Peri-operative Medicine and Biomedical Data Sciences, Stanford University, Stanford, CA, USA.

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