Work

Interpretable multi-task prediction in rhabdomyolysis: promise and caveats

Year: 2026

Type: article

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.

Source: EClinicalMedicine

DOI: 10.1016/j.eclinm.2026.103896

Authors: Jiachuan Xiong, Jinghong Zhao

Institutions: Army Medical University, Xinqiao Hospital

Language: English

Topic: Muscle and Compartmental Disorders

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,780