Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic health records and computed tomography scans.

Abstract

Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer survivors [2]. The outcomes of CVD mortality among cancer patients, particularly those with breast cancer, highlight the need for early detection of CVD at the beginning of cancer treatment as cardiotoxicity can also lead to accelerated development of chronic diseases, especially in the presence of risk factors [3]. A fusion deep learning model was developed and tested using computed tomography (CT) scans and electronic health records (EHR) for CVD mortality prediction in breast cancer patients undergoing radiation therapy. The model utilizes computed tomography (CT) scans and electronic health records (EHR) for CVD mortality prediction in breast cancer patients undergoing radiation therapy. A cohort of 23,067 patients consisting of ∼5 million CT slices and ∼600,000 EHR documents was used for the model development and testing. Performance of the model is assessed using the AUC and accuracy at a 95% confidence level. The fusion model achieves an AUC of 0.946 [0.939---0.950], and accuracy of, 0.93 [0.92 - 0.94] at 95% confidence interval (CI). These results show that fusion models can learn versatile representations from medical images and medical text documents and can effectively be combined for tasks like predicting CVD mortality with higher accuracy when employing the appropriate fusion strategy.