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ARXIV:2603.16723 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16723MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy.
Opportunity summary
Pain Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security.
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: Our federated learning models demonstrated strong predictive performance, with AUROC scores consistently comparable or superior performance in terms of AUROC and AUPRC across…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy.
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Paper Pack
10.48550/arXiv.2603.16723Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy.
Abstract
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security. Methods: This retrospective, longitudinal, multicenter cohort study included 358,644 adult patients admitted to five healthcare institutions, who underwent 494,163 inpatient major surgical procedures from 2012-2023. We developed and internally and externally validated federated learning models to predict the postoperative risk of intensive care unit (ICU) admission, mechanical ventilation (MV) therapy, acute kidney injury (AKI), and in-hospital mortality. These models were compared with local models trained on data from a single center and central models trained on a pooled dataset from all centers. Performance was primarily evaluated using area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC) values. Results: Our federated learning models demonstrated strong predictive performance, with AUROC scores consistently comparable or superior performance in terms of AUROC and AUPRC across all outcomes and sites. Our federated learning models also demonstrated strong generalizability, with comparable or superior performance in terms of both AUROC and AUPRC compared to the best local learning model at each site. Conclusions: By leveraging multicenter data, we developed robust, generalizable, and privacy-preserving predictive models for major postoperative complications and mortality. These findings support the feasibility of federated learning in clinical decision support systems.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security.
METHOD
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models will offer robust generali...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: Our federated learning models demonstrated strong predictive performance, with AUROC scores consistently comparable or superior performance in terms of AUROC and AUPRC across all outcomes and sit...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: Our federated learning models demonstrated strong predictive performance, with AUROC scores consistently comparable or superior performance in terms of AUROC and AUPRC across all outcomes and sites.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Federated learning models for predicting major postoperative complications using multicenter data while preserving patient privacy.
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Medical AI
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7.0/10 public viability
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reason
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