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Predicting sepsis outcomes early is vital. Despite machine learning\u2019s (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. We aimed to devise a prognostic model, leveraging MIMIC-IV data, to predict sepsis mortality and validate it in a Chinese teaching hospital. MIMIC-IV provided patient data, split into training and internal validation sets. Four ML models logistic regression (LR), support vector machine (SVM), deep neural networks (DNN), and extreme gradient boosting (XGBoost) were employed. Shapley additive interpretation offered early and interpretable mortality predictions. Area under the ROC curve (AUROC) gaged predictive performance. Results were cross verified in a Chinese teaching hospital. The study included 27,134 sepsis patients from MIMIC-IV and 487 from China. After comparing, 52 clinical indicators were selected for ML model development. All models exhibited excellent discriminative ability. XGBoost surpassed others, with AUROC of 0.873 internally and 0.844 externally. XGBoost outperformed other ML models (LR: 0.829; SVM: 0.830; DNN: 0.837) and clinical scores (Simplified Acute Physiology Score II: 0.728; Sequential Organ Failure Assessment: 0.728; Oxford Acute Severity of Illness Score: 0.738; Glasgow Coma Scale: 0.691). XGBoost\u2019s hospital mortality prediction achieved AUROC 0.873, sensitivity 0.818, accuracy 0.777, specificity 0.768, and F1 score 0.551. We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.<\/jats:p>","DOI":"10.1007\/s11739-024-03732-2","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T11:02:00Z","timestamp":1723633320000},"page":"909-918","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study"],"prefix":"10.1007","volume":"20","author":[{"given":"Yiping","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongqiu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"3732_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.ajem.2021.02.014","volume":"45","author":"Y Wang","year":"2021","unstructured":"Wang Y, Sun F, Hong G, Lu Z (2021) Thyroid hormone levels as a predictor marker predict the prognosis of patients with sepsis. 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The study was approved by the First Affiliated Hospital Ethics Committee (Issuing Number:KY2022-R138). The need for informed consent was waived by the First Affiliated Hospital of Wenzhou Medical University Ethics Committee in Clinical Research for this retrospective study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The Massachusetts Institute of Technology (Cambridge, MA, USA) and the Institutional Review Board of BIDMC (Boston, MA, USA) approved the use of the MIMIC-IV database for authorized users. This database is a public de-identified database thus informed consent and approval of the Institutional Review Board was waived.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not commissioned, externally peer reviewed.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Provenance and peer review"}},{"value":"All procedures performed in the present study were in accordance with the principles outlined in the 1964 Helsinki Declaration and its later amendments. The study was approved by the First Affiliated Hospital Ethics Committee (Issuing Number:KY2022- R138). The need for informed consent was waived by the First Affiliated Hospital of Wenzhou Medical University Ethics Committee in Clinical Research for this retrospective study.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights Statement"}},{"value":"The Massachusetts Institute of Technology (Cambridge, MA, USA) and the Institutional Review Board of BIDMC (Boston, MA, USA) approved the use of the MIMIC-IV database for authorized users. This database is a public de-identified database thus informed consent and approval of the Institutional Review Board was waived.","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}