[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125298-en":3,"doc-seo-125298-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125298,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data","Explainable pseudo-dynamic machine learning is developed to predict ICU mortality in myocardial infarction patients using two retrospective cohorts from eICU and MIMIC-IV. An integrated XGBoost model within an EHR time-series extraction framework (XMI-ICU) achieves high discrimination up to 24 hours before the event, with time-resolved interpretability based on Shapley values. Performance is validated on a heldout eICU test set and externally confirmed on MIMIC-IV without prior training. The framework is also compared with APACHE IV for clinical risk analysis.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nExplainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data  \nMunib Mesinovic1􀀍, Peter Watkinson2 & Tingting Zhu1  \nMyocardial infarction (MI) remains one of the greatest contributors to mortality, and patients admitted to the intensive care unit (ICU) with myocardial infarction are at higher risk of death. In this study, we use two retrospective cohorts extracted from two US-based ICU databases, eICU and MIMIC-IV, to develop an explainable pseudo-dynamic machine learning framework for mortality prediction in the  \nICU. The method provides accurate prediction for ICU patients up to 24 hours before the event and provides time-resolved interpretability. We compare standard supervised machine learning algorithms with novel tabular deep learning approaches and find that an integrated XGBoost model in our EHR time-series extraction framework (XMI-ICU) performs best. The framework was evaluated on a heldout test set from eICU and externally validated on the MIMIC-IV cohort using the most important features identified by time-resolved Shapley values. XMI-ICU achieved AUROCs of 92.0 (balanced accuracy of 82.3) for a 6-hour prediction of mortality. We demonstrate that XMI-ICU maintains reliable predictive performance across different prediction horizons (6, 12, 18, and 24 hours) during ICU stay while also achieving successful external validation in a separate patient cohort from MIMICIV without any previous training on that dataset. We also evaluated the framework for clinical risk analysis by comparing it to the standard APACHE IV system in active use. We show that our framework successfully leverages time-series physiological measurements from ICU health records by translating them into stacked static prediction problems for mortality in heart attack patients and can offer clinical insight from time-resolved interpretability through the use of Shapley values.  \nKeywords Explainability, Icare, Machine learning, Myocardial infarction, Prediction  \nAcute myocardial infarction (AMI) encompasses a spectrum of clinical presentations including ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and acute coronary syndrome with confirmed myocardial damage. myocardial infarction remains one of the greatest contributors to cardiovascular deaths in the world whose incidence remains critically high with approximately every 40 seconds someone in the United States suffering an episode1. Cardiovascular diseases (CVDs) also represent a major cost burden globally with MI in the ICU being one of the most common CVD-related conditions in the critical care system2. In 2015, there were more than 18 million CVD-related deaths with MI accounting for over 15% of overall mortality and research showing that healthcare costs skyrocket with longer and more inefficient treatment in the ICU3–5. Patients who exhibit MI are usually referred to the ICU, however, they are 10% more likely to suffer another episode in the days following and are at higher risk of death, especially the elderly6. For STEMI, NSTEMI, and general AMI patients admitted to the ICU, studies have found mortality rates as high as 17.6% but usually closer to 11% within the ICU in multicentre ICU studies7–10. Mortality prediction models can help with prioritising patients with myocardial infarction and supplant existing mortality prediction tools like the APACHE system deployed in US critical care centres, which has been criticised as too general and inaccurate for specific populations and diseases11, 12. Others like the Framingham Risk and the GRACE score are simple linear risk calculators for general mortality for heart disease patients and do not support individual prognosis from longitudinal patient data13, 14. Machine learning would allow us to provide individual prognosis while learning from complex l","cbCainZasFmySiCd","https://ap.wps.com/l/cbCainZasFmySiCd","pdf",4408173,1,15,"English","en",105,"# Background and problem\n## Clinical context of myocardial infarction in ICU\n## Limitations of existing mortality tools and static models\n# Proposed method\n## Explainable pseudo-dynamic framework\n## XMI-ICU time-series extraction and integrated XGBoost\n# Evaluation and validation\n## Heldout testing on eICU\n## External validation on MIMIC-IV\n## Prediction horizons and AUROC results\n# Interpretability and clinical risk analysis\n## Time-resolved Shapley value interpretation\n## Comparison with APACHE IV","[{\"question\":\"What data sources are used to build and validate the mortality prediction framework?\",\"answer\":\"Two retrospective cohorts are extracted from US-based ICU databases, eICU and MIMIC-IV. The framework is evaluated on a heldout eICU test set and externally validated on MIMIC-IV.\"},{\"question\":\"How does XMI-ICU provide time-resolved interpretability?\",\"answer\":\"XMI-ICU uses time-resolved interpretability based on Shapley values, identifying the most important features across the prediction window.\"},{\"question\":\"How is the model’s performance assessed across different prediction horizons?\",\"answer\":\"Mortality prediction is demonstrated for multiple horizons (6, 12, 18, and 24 hours) during ICU stay, with AUROC and balanced accuracy reported for a 6-hour prediction and reliable performance maintained across horizons.\"}]","Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data | PDF",1785898055,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"explainable-machine-learning-for-predicting-icu-mortality-in-myocardial-infarction-patients-using-pseudo-dynamic-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/explainable-machine-learning-for-predicting-icu-mortality-in-myocardial-infarction-patients-using-pseudo-dynamic-data/125298/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data sources are used to build and validate the mortality prediction framework?","Question",{"text":75,"@type":76},"Two retrospective cohorts are extracted from US-based ICU databases, eICU and MIMIC-IV. The framework is evaluated on a heldout eICU test set and externally validated on MIMIC-IV.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does XMI-ICU provide time-resolved interpretability?",{"text":80,"@type":76},"XMI-ICU uses time-resolved interpretability based on Shapley values, identifying the most important features across the prediction window.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model’s performance assessed across different prediction horizons?",{"text":84,"@type":76},"Mortality prediction is demonstrated for multiple horizons (6, 12, 18, and 24 hours) during ICU stay, with AUROC and balanced accuracy reported for a 6-hour prediction and reliable performance maintained across horizons.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]