{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T23:37:33Z","timestamp":1792193853744,"version":"4.4.0"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aimed to develop an interpretable model to predict the mortality risk for severe pneumonia patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study retrospectively employed data from severe pneumonia patients at two hospitals as the training set for the model development. Patients with severe pneumonia admitted from the same two hospitals were prospectively included as the test set for the model evaluation. A total of 115 candidate features were extracted based on clinical relevance and existing literature. The least absolute shrinkage and selection operator (LASSO) regression was applied to select features for the establishment of five models: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting (XGBoost). The performance of the models was assessed from discrimination, calibration and clinical practicability. The optimal model was screened out, and SHapley Additive exPlanation (SHAP) method was used to explain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 323 eligible patients with severe pneumonia were enrolled, including 226 patients in the training set and 97 in test set. In comparison to the other four models, the XGBoost model demonstrated the third highest area under the receiver operating characteristic (AUROC), along with optimal calibration and clinical practicability. The SHAP value of the XGBoost model indicated that the application of retention catheterization was identified as the most important influential predictor in the model, followed by oral Chinese herbal decoction, blood urea nitrogen (BUN) level, age, application of tracheotomy, complication of septic shock, and TCM syndrome (pathogenic qi falling into and prostration syndrome).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Older age, increased BUN level, complication of septic shock, tracheotomy application, retention catheterization application, oral Chinese herbal decoction, and TCM syndrome (pathogenic qi falling into and prostration syndrome) may be potential risk factors that affect mortality in severe pneumonia, while application of tracheotomy and oral Chinese herbal decoction were associated with reduced mortality. The XGBoost model exhibits superior overall performance in predicting hospital mortality risk for severe pneumonia, greater than traditional scoring systems such as Pneumonia Severity Index (PSI), Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II), which assists clinicians in prognostic assessment, resulting in improved therapeutic strategies and optimal resource allocation for patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2025.1660893","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T06:33:20Z","timestamp":1764138800000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning"],"prefix":"10.3389","volume":"16","author":[{"given":"Kai","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiajin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxuan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.clnesp.2021.08.030","article-title":"mNUTRIC tool is capable to predict nutritional needs and mortality early in patients suffering from severe pneumonia","volume":"45","author":"Acehan","year":"2021","journal-title":"Clin. 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