TY - JOUR
T1 - Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity
AU - Cardio-Oncology Artificial Intelligence Informatics & Precision (CAIP) Research Team Investigators
AU - Brown, Sherry Ann
AU - Sparapani, Rodney
AU - Osinski, Kristen
AU - Zhang, Jun
AU - Blessing, Jeffrey
AU - Cheng, Feixiong
AU - Hamid, Abdulaziz
AU - Berman, Generika
AU - Lee, Kyla
AU - BagheriMohamadiPour, Mehri
AU - Castrillon Lal, Jessica
AU - Kothari, Anai N.
AU - Caraballo, Pedro
AU - Noseworthy, Peter
AU - Johnson, Roger H.
AU - Hansen, Kathryn
AU - Sun, Louise Y.
AU - Crotty, Bradley
AU - Cheng, Yee Chung
AU - Olson, Jessica
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/1
Y1 - 2022/1
N2 - Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.
AB - Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.
KW - Artificial intelligence
KW - Cancer survivorship
KW - Cardio-oncology
KW - Informatics
KW - Team science
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U2 - 10.1016/j.ahjo.2022.100094
DO - 10.1016/j.ahjo.2022.100094
M3 - Article
AN - SCOPUS:85146432200
SN - 2666-6022
VL - 13
JO - American Heart Journal Plus: Cardiology Research and Practice
JF - American Heart Journal Plus: Cardiology Research and Practice
M1 - 100094
ER -