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Construction and validation of a machine learning-based model predicting early readmission in patients with decompensated cirrhosis: a prospective two-center cohort study.

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Názov: Construction and validation of a machine learning-based model predicting early readmission in patients with decompensated cirrhosis: a prospective two-center cohort study.
Autori: Yang, Fang, Li, Jia, Yang, Ziyi, Wu, Liping, Wang, Han, Sun, Chao
Zdroj: BioData Mining; 9/24/2025, Vol. 18 Issue 1, p1-17, 17p
Predmety: MACHINE learning, RANDOM forest algorithms, PATIENT readmissions, HEALTH outcome assessment, LONGITUDINAL method, CHRONIC active hepatitis
Abstrakt: Background: Early 30-day readmission remains a significant burden on the socioeconomic and healthcare system in the context of decompensated cirrhosis. Early recognition and accurate identification are crucial. However, current evidence is elusive and traditional scores concerning liver disease severity are lacking specificity and sensitivity. We sought to construct and validate an explainable machine learning (ML)-based prediction model, and evaluate its prognostic implementation in patients readmitted due to acute episodes. The prediction model for discovery and validation was based on a two-center prospective investigation. Our discovery sample, comprising 636 patients with cirrhosis, was divided into a training set and a test set, with an additional cohort of 150 patients serving as an external validation. Eleven ML methods were performed to establish an indicative model based on a variety of easily accessible and obtainable variables from the electronic health record. The area under the ROC curve (AUC), alongside several evaluation parameters, was used for comparison regarding predictive performance. Considering feature importance and final model explanation, we adopted the SHapley Additive exPlanation method for ranking. Furthermore, prognostic implementation was verified by subgrouping according to the final model and clinical outcomes during follow-up. Results: Among all 11 ML algorithms, the random forest (RF) algorithm represented the best discriminatory capability. Processing feature reduction generated a final 7-feature RF model with explainability based on the importance ranking. Our constructed model was of moderately accurate prediction pertaining to internal and external validations, with respective AUCs of 0.853 and 0.838, which was further transformed into an online tool to facilitate daily practice. Patients positively adjudged by the prediction model had aggravated underlying disease severity and poor psychophysiologic reservation. Conclusions: The final explainable ML model was capable of predicting early readmission and was closely connected with adverse outcomes in individual patients experiencing decompensated cirrhosis. Notably, it allayed the "black-box" concerns inherent to ML techniques with an indirect interpretation. [ABSTRACT FROM AUTHOR]
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Databáza: Biomedical Index
Popis
Abstrakt:Background: Early 30-day readmission remains a significant burden on the socioeconomic and healthcare system in the context of decompensated cirrhosis. Early recognition and accurate identification are crucial. However, current evidence is elusive and traditional scores concerning liver disease severity are lacking specificity and sensitivity. We sought to construct and validate an explainable machine learning (ML)-based prediction model, and evaluate its prognostic implementation in patients readmitted due to acute episodes. The prediction model for discovery and validation was based on a two-center prospective investigation. Our discovery sample, comprising 636 patients with cirrhosis, was divided into a training set and a test set, with an additional cohort of 150 patients serving as an external validation. Eleven ML methods were performed to establish an indicative model based on a variety of easily accessible and obtainable variables from the electronic health record. The area under the ROC curve (AUC), alongside several evaluation parameters, was used for comparison regarding predictive performance. Considering feature importance and final model explanation, we adopted the SHapley Additive exPlanation method for ranking. Furthermore, prognostic implementation was verified by subgrouping according to the final model and clinical outcomes during follow-up. Results: Among all 11 ML algorithms, the random forest (RF) algorithm represented the best discriminatory capability. Processing feature reduction generated a final 7-feature RF model with explainability based on the importance ranking. Our constructed model was of moderately accurate prediction pertaining to internal and external validations, with respective AUCs of 0.853 and 0.838, which was further transformed into an online tool to facilitate daily practice. Patients positively adjudged by the prediction model had aggravated underlying disease severity and poor psychophysiologic reservation. Conclusions: The final explainable ML model was capable of predicting early readmission and was closely connected with adverse outcomes in individual patients experiencing decompensated cirrhosis. Notably, it allayed the "black-box" concerns inherent to ML techniques with an indirect interpretation. [ABSTRACT FROM AUTHOR]
ISSN:17560381
DOI:10.1186/s13040-025-00479-0