{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T03:54:44Z","timestamp":1783137284332,"version":"3.54.6"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.<\/jats:p>","DOI":"10.1093\/bib\/bbae491","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:32:36Z","timestamp":1727915556000},"source":"Crossref","is-referenced-by-count":11,"title":["Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis"],"prefix":"10.1093","volume":"25","author":[{"given":"Fang","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Digestive System, Baodi Clinical College of Tianjin Medical University , No.8 Guangchuan Road, Baodi District, Tianjin 301800,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geriatrics, Tianjin Hexi Hospital , No.43 Qiongzhou Road, Hexi District, Tianjin 300202,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanting","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital , Anshan Road 154, Heping District, Tianjin 300052,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumei","family":"He","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The Third People's Hospital of Chengdu , Chengdu 610031, Sichuan Province,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The Third People's Hospital of Chengdu , Chengdu 610031, Sichuan Province,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital , Anshan Road 154, Heping District, Tianjin 300052,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0380-7999","authenticated-orcid":false,"given":"Chao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital , Anshan Road 154, Heping District, Tianjin 300052,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"2024100300322764900_ref1","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1016\/S0140-6736(21)01374-X","article-title":"Liver 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