Interpretable multi-task prediction in rhabdomyolysis: promise and caveats
Abstract
We read with great interest the recent article by Liu et al., who reported the development of interpretable multi-task machine learning (ML) models for patients with rhabdomyolysis (RM). Their approach simultaneously predicted acute kidney injury (AKI), disease severity, renal-replacement therapy (RRT), and in-hospital mortality, derived from MIMIC-IV/eICU and externally validated in a four-hospital Chinese cohort. The authors further implemented compact five-variable models with web and Android prototypes and demonstrated SHAP-based interpretability, representing significant steps toward clinical translation.
Fields of Study & Subjects
Restless Legs Syndrome Research
Muscle and Compartmental Disorders
Neurological and metabolic disorders
Publication details
- Journal / source
- EClinicalMedicine
- Publisher
- Elsevier BV
- Volume
- 94
- Pages
- 103896–103896
- Publication date
- 1 April 2026
- Language
- EN