Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol

作者：Tianyu Gao, Hao Ren, Shan He, Deyi Liang, Yuming Xu, Kecheng Chen, Yufan Wang, Yuxin Zhu, Heling Dong, Zhongzhi Xu, Weiming Chen, Weibin Cheng, Fengshi Jing, Xiaoyu Tao · 发表于：Frontiers in Cardiovascular Medicine · 年份：2023 · DOI：10.3389/fcvm.2022.1091885 · 被引用次数：24 · 研究领域：Advanced Technologies in Various Fields、Cardiac Health and Mental Health、Cardiovascular Health and Risk Factors

Background: Cardiovascular disease (CVD) and cancer are the first and second causes of death in over 130 countries across the world. They are also among the top three causes in almost 180 countries worldwide. Cardiovascular complications are often noticed in cancer patients, with nearly 20% exhibiting cardiovascular comorbidities. Physical exercise may be helpful for cancer survivors and people living with cancer (PLWC), as it prevents relapses, CVD, and cardiotoxicity. Therefore, it is beneficial to recommend exercise as part of cardio-oncology preventive care. Objective: With the progress of deep learning algorithms and the improvement of big data processing techniques, artificial intelligence (AI) has gradually become popular in the fields of medicine and healthcare. In the context of the shortage of medical resources in China, it is of great significance to adopt AI and machine learning methods for prescription recommendations. This study aims to develop an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care, and this paper presents the study protocol. Methods: This will be a retrospective machine learning modeling cohort study with interventional methods (i.e., exercise prescription). We will recruit PLWC participants at baseline (from 1 January 2025 to 31 December 2026) and follow up over several years (from 1 January 2027 to 31 December 2028). Specifically, participants will be eligible if they are (1) PL...