Machine learning-based prediction of one-year mortality in ischemic stroke patients
<h3>Background</h3><p dir="ltr">Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic...
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2024
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| _version_ | 1864513539566206976 |
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| author | Ahmad Abujaber (9100064) |
| author2 | Said Yaseen (22282366) Yahia Imam (9617067) Abdulqadir Nashwan (17380348) Naveed Akhtar (4919398) |
| author2_role | author author author author |
| author_facet | Ahmad Abujaber (9100064) Said Yaseen (22282366) Yahia Imam (9617067) Abdulqadir Nashwan (17380348) Naveed Akhtar (4919398) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad Abujaber (9100064) Said Yaseen (22282366) Yahia Imam (9617067) Abdulqadir Nashwan (17380348) Naveed Akhtar (4919398) |
| dc.date.none.fl_str_mv | 2024-11-14T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1093/oons/kvae011 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_learning-based_prediction_of_one-year_mortality_in_ischemic_stroke_patients/30173008 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Health sciences Epidemiology Health services and systems ischemic stroke mortality machine learning early prediction personalized medicine |
| dc.title.none.fl_str_mv | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke. </p><h3>Methods</h3><p dir="ltr">Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model’s outcomes and defined the influential predictive factors. </p><h3>Results</h3><p dir="ltr">Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). </p><h3>Discussion</h3><p dir="ltr">The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation. </p><h3>Conclusion</h3><p dir="ltr">This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.</p><h2>Other Information</h2><p dir="ltr">Published in: Oxford Open Neuroscience<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1093/oons/kvae011" target="_blank">https://dx.doi.org/10.1093/oons/kvae011</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_da778d5a96a9148346977ea3f2dbedfd |
| identifier_str_mv | 10.1093/oons/kvae011 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30173008 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning-based prediction of one-year mortality in ischemic stroke patientsAhmad Abujaber (9100064)Said Yaseen (22282366)Yahia Imam (9617067)Abdulqadir Nashwan (17380348)Naveed Akhtar (4919398)Biomedical and clinical sciencesNeurosciencesHealth sciencesEpidemiologyHealth services and systemsischemic strokemortalitymachine learningearly predictionpersonalized medicine<h3>Background</h3><p dir="ltr">Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke. </p><h3>Methods</h3><p dir="ltr">Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model’s outcomes and defined the influential predictive factors. </p><h3>Results</h3><p dir="ltr">Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). </p><h3>Discussion</h3><p dir="ltr">The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation. </p><h3>Conclusion</h3><p dir="ltr">This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.</p><h2>Other Information</h2><p dir="ltr">Published in: Oxford Open Neuroscience<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1093/oons/kvae011" target="_blank">https://dx.doi.org/10.1093/oons/kvae011</a></p>2024-11-14T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1093/oons/kvae011https://figshare.com/articles/journal_contribution/Machine_learning-based_prediction_of_one-year_mortality_in_ischemic_stroke_patients/30173008CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301730082024-11-14T09:00:00Z |
| spellingShingle | Machine learning-based prediction of one-year mortality in ischemic stroke patients Ahmad Abujaber (9100064) Biomedical and clinical sciences Neurosciences Health sciences Epidemiology Health services and systems ischemic stroke mortality machine learning early prediction personalized medicine |
| status_str | publishedVersion |
| title | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| title_full | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| title_fullStr | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| title_full_unstemmed | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| title_short | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| title_sort | Machine learning-based prediction of one-year mortality in ischemic stroke patients |
| topic | Biomedical and clinical sciences Neurosciences Health sciences Epidemiology Health services and systems ischemic stroke mortality machine learning early prediction personalized medicine |