Description
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients’ quality of life. This study focuses on predicting the development of heart failure in cancer patients after cancer diagnoses using historical electronic health record (EHR) data. We examined four machine learning algorithms using 143,199 cancer patients from the University of Florida Health (UF Health) Integrated Data Repository (IDR). We identified a total number of 1,958 qualified cases and matched them to 15,488 controls by gender, age, race, and major cancer type. Two feature encoding strategies were compared to encode variables as machine learning features. The gradient boosting (GB) based model achieved the best AUC score of 0.9077 (with a sensitivity of 0.8520 and a specificity of 0.8138), outperforming other machine learning methods. We also looked into the subgroup of cancer patients with exposure to chemotherapy drugs and observed a lower specificity score (0.7089). The experimental results show that machine learning methods are able to capture clinical factors that are known to be associated with heart failure and that it is feasible to use machine learning methods to identify cancer patients at risk for cancer therapy-related heart failure.
Learning Objective: After participating in this session, the learner should be better able to:
1. Understand the importance of early detection of cancer patients at risk of cardiotoxicity before cardiotoxic treatments.
2. Understand how to adapt machine learning methods to detect cancer patients developing heart failure after cancer diagnoses using structured EHR data.
3. Learn practical strategies of converting high-dimension and sparse structured EHR data as machine learning features.
Authors:
Xi Yang (Presenter)
University of Florida
Yan Gong, College of Medicine, University of Florida
Nida Waheed, Internal Medicine, College of Medicine, University of Florida
Keith March, Division of Cardiovascular Medicine, College of Medicine, University of Florida
Jiang Bian, University of Florida
William Hogan, University of Florida
Yonghui Wu, University of Florida
Presentation Materials: