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This retrospective study assesses multiple machine learning models for predicting sepsis risk in ICU patients with ICH using the MIMIC-IV database, with training and internal validation cohorts and external validation using the eICU-CRD. Sepsis prognostic factors are selected via LASSO and backward stepwise logistic regression, and models are compared by predictive accuracy. Random Forest achieves the best performance with AUCs of 0.912 (training), 0.832 (internal), and 0.798 (external).",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-model-to-predict-sepsis-in-icu-patients-with-intracerebral-hemorrhage/291036/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-model-to-predict-sepsis-in-icu-patients-with-intracerebral-hemorrhage/291036.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study’s main goal?","Question",{"text":112,"@type":113},"To evaluate how machine learning models can predict sepsis risk in ICU patients with intracerebral hemorrhage (ICH).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which datasets were used for training, validation, and external testing?",{"text":117,"@type":113},"MIMIC-IV was used for retrospective training and internal validation, and eICU Collaborative Research Database (eICU-CRD) was used for external validation.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best and how was it evaluated?",{"text":121,"@type":113},"The Random Forest model outperformed others, with reported AUCs of 0.912 (training), 0.832 (internal validation), and 0.798 (external validation).",{"name":123,"@type":110,"acceptedAnswer":124},"How were sepsis prognostic factors selected?",{"text":125,"@type":113},"The study used LASSO to identify candidate factors, followed by backward stepwise logistic regression.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},291036,1789643253,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":133,"read_time":148,"preview_page_count":4},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning model to predict sepsis in ICU patients with intracerebral hemorrhage  \nLei Tang1,2,3,4,5,6,7, Ye Li1,2,3,4,5,6,7, Ji Zhang1, Feng Zhang1,2,3,4,5,6,7,8, Qiaoling Tang1,2,3,4,5,6,7, Xiangbin Zhang1,2,3,4,5,6,7, Sai Wang1,2,3,4,5,6,7, Yupeng Zhang1,2,3,4,5,6,7, Siyuan Ma1,2,3,4,5,6,7, Ran Liu1,2,3,4,5,6,7, Lei Chen1,2,3,4,5,6,7, Junyi Ma1,2,3,4,5,6,7, Xuelun Zou1,2,3,4,5,6,7,  \nTianxing Yao1,2,3,4,5,6,7, Rongmei Tang1,2,3,4,5,6,7, Huifang Zhou1,2,3,4,5,6,7, Lianxu Wu1,2,3,4,5,6,7, Yexiang Yi1,2,3,4,5,6,7, Yi Zeng9, Duolao Wang10􀀍 & Le Zhang1,2,3,4,5,6,7􀀍  \nPatients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. We conducted a retrospective analysis on ICH patients using the MIMIC-IV database, randomly dividing them into training and validation cohorts. We identified sepsis prognostic factors using Least Absolute Shrinkage and Selection Operator (LASSO) and backward stepwise logistic regression. Several machine learning algorithms were developed and assessed for predictive accuracy, with external validation performed using the eICU Collaborative Research Database (eICU-CRD) . We analyzed 2,214 patients, including 1,550 in the training set, 664 in the validation set, and 513 for external validation using the eICU-CRD. The Random Forest (RF) model outperformed others, achieving Area Under the Curves (AUCs) of 0.912 in training, 0.832 in internal validation, and 0.798 in external validation. Neural Network and Logistic Regression models recorded training AUCs of 0.840 and 0.804, respectively. ML models, especially the RF model, effectively predict sepsis in ICU patients with ICH, enabling early identification and management of high-risk cases.  \nKeywords Intracerebral hemorrhage, Sepsis, Machine learning, Prediction model  \nIntracerebral hemorrhage (ICH) represents about 15% of all stroke cases yet is associated with significant mortality, accounting for roughly 2.8 million deaths globally each year1,2. Approximately 11–31% of ICH cases lead to infections and long-term disabilities3,4, often exacerbating into sepsis due to immunosuppressioninduced systemic inflammations and metabolic disorders5,6.  \nIn the intensive care unit (ICU), approximately 25% of ICH patients who develop sepsis succumb within 28 days. Furthermore, sepsis is strongly associated with worsened long-term functional outcomes. These findings highlight the critical importance of early detection and effective management of sepsis in ICH patients7. A retrospective cohort study revealed that about 28% of ICH patients would develop sepsis, which poses significant challenges in their clinical management8. Sepsis is associated with an increased risk of a systemic infectious encephalopathy called sepsis-associated encephalopathy (SAE)9, leading to in-hospital coma events and a higher risk of complications in ICH patients10.  \nThe initial clinical manifestations of sepsis are not specific, and the disease progresses rapidly11, 12. Currently, there are no effective treatments, highlighting the significance of early detection and appropriate management to mitigate its impact13–15. A comprehensive understanding of the etiology of sepsis after ICH is essential for improving targeted prevention and treatment. However, the underlying cause of sepsis has not been fully  \n1Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang 330006, Jiangxi, China. 2Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.  \n3Multi-Modal Monitoring Technology for Severe Cerebrovascular Disease of Human Engineering Research Center, Changsha, Hunan, China. 4Brain Health Center of Hunan Province, Changsha, Hunan, China. 5Human Brain Disease Biological Resources Platform","cbCaivru54ePcfUv","https://ap.wps.com/l/cbCaivru54ePcfUv","pdf",1758882,11,"English","# Background\n## Burden and clinical significance of ICH-associated sepsis\n# Methods\n## Retrospective cohorts and data sources (MIMIC-IV, eICU-CRD)\n## Feature selection and model development\n# Results\n## Model performance and external validation\n# Conclusion\n## Early identification and management of high-risk cases","[{\"question\":\"What is the study’s main goal?\",\"answer\":\"To evaluate how machine learning models can predict sepsis risk in ICU patients with intracerebral hemorrhage (ICH).\"},{\"question\":\"Which datasets were used for training, validation, and external testing?\",\"answer\":\"MIMIC-IV was used for retrospective training and internal validation, and eICU Collaborative Research Database (eICU-CRD) was used for external validation.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"The Random Forest model outperformed others, with reported AUCs of 0.912 (training), 0.832 (internal validation), and 0.798 (external validation).\"},{\"question\":\"How were sepsis prognostic factors selected?\",\"answer\":\"The study used LASSO to identify candidate factors, followed by backward stepwise logistic regression.\"}]","Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage | PDF",28]