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The objectives were to develop a machine learning model based on nutritional assessment for the prediction of all-cause death and infectious complications.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A cohort of older patients with possible sarcopenia (divided into training group [70%] and validation group [30%]) from 30 hospitals in 14 major cities in China was retrospectively analyzed. Clinical characteristics, laboratory examination, Nutritional risk Screening-2002 (NRS-2002) and mini-nutritional Assessment-Short form (MNA-SF) were used to construct machine learning models to predict in-hospital adverse events, including all-cause mortality and infectious complications. The applied algorithms included decision tree, random forest, gradient boosting machine (GBM), LightGBM, extreme gradient boosting and neural network. Model performance was assessed according to learning a series of learning metrics including area under the receiver operating characteristic curve (AUC) and accuracy.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Among 3 999 participants (mean age 75.89 years [SD 7.14]; 1 805 [45.1%] were female), 373 (9.7%) had adverse events, including 62 (1.6%) of in-hospital death and 330 (8.5%) of infectious complications. The decision tree model showed a better AUC of 0.7072 (95% CI 0.6558\u20130.7586) in the validation cohort, using the five most important variables (i.e., mobility, reduced food intake, white blood cell count, upper arm circumference, and hypoalbuminemia).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Machine learning prediction models are feasible and effective for identifying adverse events, and may be helpful to guide clinical nutrition decision-making in older inpatients with possible sarcopenia.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s40520-024-02916-2","type":"journal-article","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T11:39:37Z","timestamp":1740224377000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia"],"prefix":"10.1007","volume":"37","author":[{"given":"Chengyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moxi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianchun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"2916_CR1","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jamda.2019.12.012","volume":"21","author":"L Chen","year":"2020","unstructured":"Chen L, Woo J, Assantachai P, Auyeung T, Chou M, Iijima K, Jang H, Kang L, Kim M, Kim S et al (2020) Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia diagnosis and treatment. 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