Explainable Machine Learning for Coma Outcome Prediction Based on Structural and Functional Brain MRI
A new ensemble of brain MRI-derived metrics was specifically related to coma state, its etiology, and the patient's potential for recovery at 3 months. The structural and functional integrity of mesocircuit and frontoparietal networks appeared to carry the most relevant information.
Authors
- Edouard Naboulsi
- Sylvain Cussat‐Blanc
- Giulia Maria Mattia (ORCID: https://orcid.org/0000-0002-6752-367X)
- S Silva
- Patrice Péran (ORCID: https://orcid.org/0000-0001-7200-0139)
- BEATRICE RIU
- Fanny Bounes
- Eve Cervoni
- Jean Marc Olivot
- Pablo Barttfeld
- Julie Decourt
Institutions
- Université Toulouse III - Paul Sabatier (FR)
- Universidad Nacional de Córdoba (AR)
- Université Fédérale de Toulouse Midi-Pyrénées (FR)
- Université Toulouse-I-Capitole (FR)
- Institut de Recherche en Informatique de Toulouse (FR)
- Hôpital Rangueil (FR)
- Hôpital Purpan (FR)
- Université Toulouse - Jean Jaurès (FR)
- Institut Polytechnique de Bordeaux (FR)
- Institut de Mathématiques de Toulouse (FR)
Publication Details
- Journal
- Critical Care Medicine
- Published
- 2026-02-27
- DOI
- https://doi.org/10.1097/ccm.0000000000007068
- Primary Topic
- Traumatic Brain Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00