# An interpretable machine learning model for predicting 28-day in-hospital mortality in ICU patients with sepsis and atrial fibrillation

Source: [Medical research : nature.com subject feeds](https://www.nature.com/articles/s41598-026-79602-y) · 2026-11-10 · Medicine
Page: https://themorning.science/story/an-interpretable-machine-learning-model-for-predicting-28-day-in-hospital-mortal-0b0187754473beb3
Kind: research · Impact: low

## In brief

- A Scientific Reports article describes an interpretable machine learning model for predicting 28-day in-hospital mortality.
- The study population is ICU patients with sepsis and atrial fibrillation.
- The source provided no summary, so model design, performance and validation details are not available here.

## Why it matters

It targets a high-risk group of ICU patients, and interpretability may help clinicians see which factors drive a predicted mortality risk.

## Coverage

- [An interpretable machine learning model for predicting 28-day in-hospital mortality in ICU patients with sepsis and atrial fibrillation](https://www.nature.com/articles/s41598-026-79602-y) — Medical research : nature.com subject feeds, 2026-11-10

Tags: sepsis, atrial fibrillation, machine learning, icu, mortality prediction

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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