ArticleNPJ digital medicine2025
Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Development and validation of a machine learning-based prediction model for malnutrition risk in peritoneal dialysis patients: a multi‑center retrospective study.International urology and nephrology · 2026Article
- Interpreting the clinical utility and generalizability of a multitask perioperative prediction model.NPJ digital medicine · 2026Article
- From innovation to implementation: Addressing the AI adoption gap in cardiac surgery.American heart journal plus : cardiology research and practice · 2026Review
- Interpretable multi-model deep learning framework for automated four-class diagnosis of ocular toxoplasmosis using fundus imaging.Scientific reports · 2025Article
- DiabCompSepsAI: Integrated AI Model for Early Detection and Prediction of Postoperative Complications in Diabetic Patients-Using a Random Forest Classifier.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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 in cohort B). We also elucidated the contribution of each input variable to predictions among different outcomes. Our findings highlight the potential of multitask learning to streamline preoperative risk assessment and present a scalable, interpretable, and generalizable framework for improving perioperative care.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.