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Federated learning for COVID-19 mortality prediction in a multicentric sample of 21 hospitals

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  • Roberta Moreira Wichmann
  • Murilo Afonso Robiati Bigoto
  • Alexandre Dias Porto Chiavegatto Filho

Abstract

We evaluated Federated Learning (FL) strategies for predicting COVID-19 mortality using a multicenter sample of 17,022 patients from 21 diverse Brazilian hospitals. We tested horizontal FL architectures employing Logistic Regression (LR) and a Multi-Layer Perceptron (MLP) via parameter aggregation, alongside a novel Federated Random Forest (RF) using ensemble aggregation. Performance gain (ΔAUC, calculated as AUC federated minus AUC local) was quantified using bootstrap analysis to determine 95% confidence intervals. FL models demonstrated a beneficial collaborative effect. The average ΔAUC across the network was +0.0018 for LR, +0.0599 for MLP, and +0.0528 for RF. Crucially, the gain’s magnitude and statistical significance showed a strong inverse correlation with local patient volume (N). Substantial and statistically significant gains concentrated in data-limited institutions (N

Suggested Citation

  • Roberta Moreira Wichmann & Murilo Afonso Robiati Bigoto & Alexandre Dias Porto Chiavegatto Filho, 2025. "Federated learning for COVID-19 mortality prediction in a multicentric sample of 21 hospitals," PLOS Computational Biology, Public Library of Science, vol. 21(11), pages 1-16, November.
    • Handle: RePEc:plo:pcbi00:1013695
    DOI: 10.1371/journal.pcbi.1013695
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