aceDevelopment and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
Background Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 hour intensive care unit (ICU) admission. Methods We used data from MIMIC-III, MIMIC-IV, eICU, and HiRID. A multimodal model was developed on the MIMIC datasets, featuring time series compon…
A multimodal deep learning model using first-24-hour ICU data predicted subsequent in-hospital mortality with high discrimination, and adding clinical notes and chest X-ray images further improved performance.
Evidence
- Peer-reviewedAnesthesiology2026-08-04
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Truvace Impact Record TRV-2026-0656, v1: “Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data.” Truvace, 2026-08-05. /record/TRV-2026-0656 (accessed at citation time). sha256 e145c84d483a0454…
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