Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review
<h3>Purpose of Review</h3><p dir="ltr">This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to under...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| _version_ | 1864513530555793408 |
|---|---|
| author | Sarah Aqel (17787809) |
| author2 | Sebawe Syaj (11760737) Ayah Al-Bzour (15158056) Faris Abuzanouneh (17787812) Noor Al-Bzour (17787815) Jamil Ahmad (327791) |
| author2_role | author author author author author |
| author_facet | Sarah Aqel (17787809) Sebawe Syaj (11760737) Ayah Al-Bzour (15158056) Faris Abuzanouneh (17787812) Noor Al-Bzour (17787815) Jamil Ahmad (327791) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sarah Aqel (17787809) Sebawe Syaj (11760737) Ayah Al-Bzour (15158056) Faris Abuzanouneh (17787812) Noor Al-Bzour (17787815) Jamil Ahmad (327791) |
| dc.date.none.fl_str_mv | 2023-10-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11886-023-01964-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Artificial_Intelligence_and_Machine_Learning_Applications_in_Sudden_Cardiac_Arrest_Prediction_and_Management_A_Comprehensive_Review/24998285 |
| 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 Cardiovascular medicine and haematology Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Sudden cardiac arrest Artificial intelligence Machine learning Prediction models Cardiopulmonary resuscitation |
| dc.title.none.fl_str_mv | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Purpose of Review</h3><p dir="ltr">This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care.</p><h3>Recent Findings</h3><p dir="ltr">The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There’s a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents.</p><h3>Summary</h3><p dir="ltr">AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.</p><h2>Other Information</h2><p dir="ltr">Published in: Current Cardiology Reports<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/10.1007/s11886-023-01964-w" target="_blank">https://dx.doi.org/10.1007/s11886-023-01964-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_55c86a8307e465bb771a3cd8d3142efa |
| identifier_str_mv | 10.1007/s11886-023-01964-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24998285 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive ReviewSarah Aqel (17787809)Sebawe Syaj (11760737)Ayah Al-Bzour (15158056)Faris Abuzanouneh (17787812)Noor Al-Bzour (17787815)Jamil Ahmad (327791)Biomedical and clinical sciencesCardiovascular medicine and haematologyClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningSudden cardiac arrestArtificial intelligenceMachine learningPrediction modelsCardiopulmonary resuscitation<h3>Purpose of Review</h3><p dir="ltr">This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care.</p><h3>Recent Findings</h3><p dir="ltr">The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There’s a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents.</p><h3>Summary</h3><p dir="ltr">AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.</p><h2>Other Information</h2><p dir="ltr">Published in: Current Cardiology Reports<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/10.1007/s11886-023-01964-w" target="_blank">https://dx.doi.org/10.1007/s11886-023-01964-w</a></p>2023-10-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11886-023-01964-whttps://figshare.com/articles/journal_contribution/Artificial_Intelligence_and_Machine_Learning_Applications_in_Sudden_Cardiac_Arrest_Prediction_and_Management_A_Comprehensive_Review/24998285CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249982852023-10-04T03:00:00Z |
| spellingShingle | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review Sarah Aqel (17787809) Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Sudden cardiac arrest Artificial intelligence Machine learning Prediction models Cardiopulmonary resuscitation |
| status_str | publishedVersion |
| title | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| title_full | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| title_fullStr | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| title_full_unstemmed | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| title_short | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| title_sort | Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Sudden cardiac arrest Artificial intelligence Machine learning Prediction models Cardiopulmonary resuscitation |