Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.

ABSTRACT: Predicting multiple postoperative complications remains challenging in perioperative care. Current approaches often address complications individually, limiting the potential for integrated risk assessment. We developed and externally validated a scalable, interpretable, tree-based multitask learning model to predict three critical postoperative outcomes-acute kidney injury (AKI), postoperative respiratory failure (PRF), and in-hospital mortality-using 16 preoperative features generally available in electronic health records. Our model achieved AUROCs of 0.805, 0.789, and 0.863 for AKI; 0.886, 0.925, and 0.911 for PRF; and 0.907, 0.913, and 0.849 for mortality in the derivation cohort and external validation cohorts A and B, respectively (all p < 0.001, except for AKI in derivation and PRF

SUBMITTER: Yoon HK 

PROVIDER: S-EPMC12485121 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature