Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline
A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2023-10-01
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| Series: | Reviews in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/RCM/24/10/10.31083/j.rcm2410296 |
| _version_ | 1797643462010470400 |
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| author | Yi Zheng Ziliang Chen Shan Huang Nan Zhang Yueying Wang Shenda Hong Jeffrey Shi Kai Chan Kang-Yin Chen Yunlong Xia Yuhui Zhang Gregory Y.H. Lip Juan Qin Gary Tse Tong Liu |
| author_facet | Yi Zheng Ziliang Chen Shan Huang Nan Zhang Yueying Wang Shenda Hong Jeffrey Shi Kai Chan Kang-Yin Chen Yunlong Xia Yuhui Zhang Gregory Y.H. Lip Juan Qin Gary Tse Tong Liu |
| author_sort | Yi Zheng |
| collection | DOAJ |
| description | A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future. |
| first_indexed | 2024-03-11T14:16:13Z |
| format | Article |
| id | doaj.art-5c1d3f5b3e45460fbb3ba02eb4d82863 |
| institution | Directory Open Access Journal |
| issn | 1530-6550 |
| language | English |
| last_indexed | 2024-03-11T14:16:13Z |
| publishDate | 2023-10-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj.art-5c1d3f5b3e45460fbb3ba02eb4d828632023-11-01T06:42:26ZengIMR PressReviews in Cardiovascular Medicine1530-65502023-10-01241029610.31083/j.rcm2410296S1530-6550(23)01018-9Machine Learning in Cardio-Oncology: New Insights from an Emerging DisciplineYi Zheng0Ziliang Chen1Shan Huang2Nan Zhang3Yueying Wang4Shenda Hong5Jeffrey Shi Kai Chan6Kang-Yin Chen7Yunlong Xia8Yuhui Zhang9Gregory Y.H. Lip10Juan Qin11Gary Tse12Tong Liu13Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaNational Institute of Health Data Science at Peking University, Peking University, 100871 Beijing, ChinaCardio-Oncology Research Unit, Cardiovascular Analytics Group, PowerHealth Limited, 999077 Hong Kong, ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaDepartment of Cardiology, First Affiliated Hospital of Dalian Medical University, 116011 Dalian, Liaoning, ChinaHeart Failure Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, ChinaLiverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L69 3BX Liverpool, UKSection of Cardio-Oncology & Immunology, Division of Cardiology and the Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA 94143, USATianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, ChinaA growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.https://www.imrpress.com/journal/RCM/24/10/10.31083/j.rcm2410296cardio-oncologymachine learningcardiotoxicityinequitymultidisciplinary team |
| spellingShingle | Yi Zheng Ziliang Chen Shan Huang Nan Zhang Yueying Wang Shenda Hong Jeffrey Shi Kai Chan Kang-Yin Chen Yunlong Xia Yuhui Zhang Gregory Y.H. Lip Juan Qin Gary Tse Tong Liu Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline Reviews in Cardiovascular Medicine cardio-oncology machine learning cardiotoxicity inequity multidisciplinary team |
| title | Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline |
| title_full | Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline |
| title_fullStr | Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline |
| title_full_unstemmed | Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline |
| title_short | Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline |
| title_sort | machine learning in cardio oncology new insights from an emerging discipline |
| topic | cardio-oncology machine learning cardiotoxicity inequity multidisciplinary team |
| url | https://www.imrpress.com/journal/RCM/24/10/10.31083/j.rcm2410296 |
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