{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T01:08:16Z","timestamp":1793149696149,"version":"4.5.0"},"reference-count":56,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often experience long-term cognitive and behavioral issues that impact their quality of life and place a burden on their families. This study aimed to develop and validate an interpretable machine learning model for early prediction of SAD in critically ill patients. Additionally, we constructed an online risk calculator to facilitate real-time clinical assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study is a retrospective analysis utilizing data from 16,120 patients in the Medical Information Mart for Intensive Care IV database. To manage imbalanced data, we applied the Synthetic Minority Over-sampling Technique (SMOTE) method. Feature selection was conducted using Multivariate Logistic Regression, LASSO regression, and the Boruta algorithm. We developed predictive models using eight machine learning algorithms and selected the best one for validation. The SHapley Additive exPlanations (SHAP) method was used for visualization and interpretation, enhancing the clinical understanding of the model, alongside the creation of an online web calculator.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We combined three feature selection methods to identify 17 key features for our machine learning prediction model. The Gradient Boosting Machine (GBM) model demonstrated excellent calibration and strong predictive accuracy in the validation cohort. The SHAP feature importance ranking revealed five critical risk factors for predicting outcomes: Glasgow Coma Scale (GCS), ICU stay duration, chloride, sodium, and Sequential Organ Failure Assessment (SOFA). Based on this optimal model, we successfully developed an online web calculator.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed and validated a machine learning model capable of accurately predicting SAD with high clinical applicability. The integration of interpretable machine learning and an online calculator offers a practical tool to support early identification and timely management of SAD in critically ill patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0323831","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T17:25:30Z","timestamp":1752686730000},"page":"e0323831","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator"],"prefix":"10.1371","volume":"20","author":[{"given":"Lang","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang Dong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing Yi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi Jun","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun Ruo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Ying","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1062-7999","authenticated-orcid":true,"given":"Ya Xin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"issue":"4","key":"pone.0323831.ref001","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e26570","article-title":"The prediction of in-hospital mortality in elderly patients with sepsis-associated acute kidney injury utilizing machine learning models","volume":"10","author":"J Tang","year":"2024","journal-title":"Heliyon"},{"issue":"3","key":"pone.0323831.ref002","doi-asserted-by":"crossref","DOI":"10.3390\/jcm9030703","article-title":"Sepsis-associated encephalopathy: from delirium to dementia?","volume":"9","author":"HY Chung","year":"2020","journal-title":"J Clin Med"},{"issue":"3","key":"pone.0323831.ref003","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1164\/rccm.201504-0781OC","article-title":"Assessment of global incidence and mortality of hospital-treated sepsis. 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