# Preserving explainability in federated learning for clinical decision-making: SHAP explainability consistency analysis of ICU mortality prediction models

Source: [Medical research : nature.com subject feeds](https://www.nature.com/articles/s41598-026-79688-4) · 2026-11-12 · Medicine
Page: https://themorning.science/story/preserving-explainability-in-federated-learning-for-clinical-decision-making-sha-c959e103023c4577
Kind: research · Impact: low

## In brief

- A nature.com article examines explainability consistency in federated learning for clinical decision-making.
- The study applies SHAP explainability consistency analysis to ICU mortality prediction models.
- The source provided no summary, so findings beyond the title are not reported here.

## Why it matters

Clinicians need explanations of model predictions to trust them, and this work examines whether those explanations stay consistent when models are trained across federated data sources.

## Timeline

- 2026-11-12: Article listed in nature.com subject feeds

## Coverage

- [Preserving explainability in federated learning for clinical decision-making: SHAP explainability consistency analysis of ICU mortality prediction models](https://www.nature.com/articles/s41598-026-79688-4) — Medical research : nature.com subject feeds, 2026-11-12

Tags: federated learning, explainable ai, shap, icu mortality, clinical decision support

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Brief generated by AI (claude-haiku-5-5) from the reporting listed under Coverage. Headlines belong to their publishers. The Morning Science – https://themorning.science