[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127354-en":3,"doc-seo-127354-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127354,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","Machine Learning for ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction","Patients with occlusion myocardial infarction (OMI) and no ST-elevation on the presenting electrocardiogram (ECG) are increasing and face poor outcomes. Immediate reperfusion therapy could help, yet current triage lacks accurate identification tools. An observational cohort study developed machine learning models for ECG diagnosis of OMI using 7,313 consecutive patients across multiple clinical sites. The externally validated model outperformed clinicians and commercial interpretation systems, improved rule-in and rule-out performance, and reclassified one in three chest-pain patients when combined with emergency clinicians’ judgment.","The Texas Medical Center Library  \nDigitalCommons@TMC  \n\n| Faculty and Staff Publications | Baylor College of Medicine |\n| --- | --- |\n| 7-1-2023\u003Cbr>Machine Learning for ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction\u003Cbr>Salah S Al-Zaiti\u003Cbr>Christian Martin-Gill\u003Cbr>Jessica K Zègre-Hemsey Zeineb Bouzid\u003Cbr>Ziad Faramand\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://digitalcommons.library.tmc.edu/baylor_docs](https://digitalcommons.library.tmc.edu/baylor_docs)\u003Cbr> Part of the Medical Biochemistry Commons, Medical Biophysics Commons, Medical Genetics Commons, Medical Specialties Commons, and the Translational Medical Research Commons |  |\n\nRecommended Citation  \nAl-Zaiti, Salah S; Martin-Gill, Christian; Zègre-Hemsey, Jessica K; Bouzid, Zeineb; Faramand, Ziad; Alrawashdeh, Mohammad O; Gregg, Richard E; Helman, Stephanie; Riek, Nathan T; Kraevsky-Phillips, Karina; Clermont, Gilles; Akcakaya, Murat; Sereika, Susan M; Van Dam, Peter; Smith, Stephen W; Birnbaum, Yochai; Saba, Samir; Sejdic, Ervin; and Callaway, Clifton W, \"Machine Learning for ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction\" (2023) . Faculty and Staff Publications. 958.  \n[https://digitalcommons.library.tmc.edu/baylor_docs/958](https://digitalcommons.library.tmc.edu/baylor_docs/958)  \nThis Article is brought to you for free and open access by the Baylor College of Medicine at  \nDigitalCommons@TMC. It has been accepted for inclusion in Faculty and Staff Publications by an authorized administrator of DigitalCommons@TMC. For more information, please contact [digcommons@library.tmc.edu](digcommons@library.tmc.edu).  \nAuthors  \nSalah S Al-Zaiti, Christian Martin-Gill, Jessica K Zègre-Hemsey, Zeineb Bouzid, Ziad Faramand, Mohammad O Alrawashdeh, Richard E Gregg, Stephanie Helman, Nathan T Riek, Karina Kraevsky-Phillips, Gilles Clermont, Murat Akcakaya, Susan M Sereika, Peter Van Dam, Stephen W Smith, Yochai Birnbaum, Samir Saba, Ervin Sejdic, and Clifton W Callaway  \nThis article is available at DigitalCommons@TMC: [https://digitalcommons.library.tmc.edu/baylor_docs/958](https://digitalcommons.library.tmc.edu/baylor_docs/958)  \nnature medicine  \nArticle [https://doi.org/10.1038/s41591-023-02396-3](https://doi.org/10.1038/s41591-023-02396-3)  \nMachine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction  \nReceived: 24 January 2023  \nAccepted: 11 May 2023  \n\n| Published online: 29 June 2023 |\n| --- |\n|  Check for updates |\n\nSalah S. Al-Zaiti  1,2,3,4 , Christian Martin-Gill  2,5, Jessica K. Zègre-Hemsey  6, Zeineb Bouzid  3, Ziad Faramand7, Mohammad O. Alrawashdeh  8,9,  \nRichard E. Gregg10, Stephanie Helman1, Nathan T. Riek  3,  \nKarina Kraevsky-Phillips  1, Gilles Clermont11, MuratAkcakaya3,  \nSusan M. Sereika1, Peter Van Dam12, Stephen W. Smith  13,14,  \nYochai Birnbaum  15, Samir Saba4,5, Ervin Sejdic16,17 & Clifton W. Callaway2,5  \nPatients with occlusion myocardial infarction (OMI) and noST-elevation on presenting electrocardiogram (ECG) are increasing in numbers.  \nThese patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validatedan intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts","cbCaicSZ5y1Jtg4A","https://ap.wps.com/l/cbCaicSZ5y1Jtg4A","pdf",3115954,3,1,26,"English","en",105,"# Background and Clinical Need\n## Study Objective and Approach\n## Model Performance and Validation\n## Risk Score and Clinical Reclassification\n## ECG Feature Interpretation and Mechanistic Links","[{\"question\":\"Why is ECG diagnosis of occlusion myocardial infarction (OMI) challenging in patients without ST-elevation?\",\"answer\":\"Patients with OMI and no ST-elevation on the initial ECG have poor prognoses and lack accurate triage tools. Existing guidelines mainly emphasize ST-segment elevation, leaving a gap for non-ST-elevation presentations.\"},{\"question\":\"How were the machine learning models developed and validated?\",\"answer\":\"Models were derived using 7,313 consecutive patients from multiple clinical sites and then externally validated. The resulting intelligent model outperformed practicing clinicians and widely used commercial interpretation systems.\"},{\"question\":\"What role does the derived OMI risk score play in routine care?\",\"answer\":\"The risk score provided enhanced rule-in and rule-out accuracy. When combined with trained emergency personnel’s clinical judgment, it helped correctly reclassify one in three patients presenting with chest pain.\"}]","Machine Learning for ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction | PDF",1785938459,66,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-for-ecg-diagnosis-and-risk-stratification-of-occlusion-myocardial-infarction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/healthcare/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-for-ecg-diagnosis-and-risk-stratification-of-occlusion-myocardial-infarction/127354/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is ECG diagnosis of occlusion myocardial infarction (OMI) challenging in patients without ST-elevation?","Question",{"text":76,"@type":77},"Patients with OMI and no ST-elevation on the initial ECG have poor prognoses and lack accurate triage tools. Existing guidelines mainly emphasize ST-segment elevation, leaving a gap for non-ST-elevation presentations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the machine learning models developed and validated?",{"text":81,"@type":77},"Models were derived using 7,313 consecutive patients from multiple clinical sites and then externally validated. The resulting intelligent model outperformed practicing clinicians and widely used commercial interpretation systems.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does the derived OMI risk score play in routine care?",{"text":85,"@type":77},"The risk score provided enhanced rule-in and rule-out accuracy. When combined with trained emergency personnel’s clinical judgment, it helped correctly reclassify one in three patients presenting with chest pain.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]