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对应用在心血管疾病风险预测中的时间到事件结果的人工智能模型的系统审查
Achamyeleh Birhanu Teshale1,2, Htet Lin Htun1, Mor Vered3
1School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
Journal of medical systems
|July 19, 2024
概括
人工智能 (AI) 模型改善了心血管疾病 (CVD) 风险预测,特别是深度学习 (DL) 模型. 未来的研究应该纳入健康的社会决定因素和性别分层,以便更好地解释AI.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 模型越来越多地用于心血管疾病 (CVD) 风险预测.
- 现有的人工智能模型往往忽视了右侧审查的数据,这是生存分析的关键因素.
- 需要进行系统审查,以评估AI模型对心血管疾病生存结果的评估,同时考虑先进的技术和数据细微差别.
研究的目的:
- 系统地审查用于CVD生存结果预测的机器学习 (ML) 和深度学习 (DL) 模型.
- 分析可解释的人工智能 (XAI) 技术的应用以及健康的社会决定因素 (SDoH) 和性别分层的纳入.
- 确定最佳的人工智能模型,并突出在心血管疾病风险预测方面的未来研究领域.
主要方法:
- 对33项使用ML和DL进行心血管疾病生存预测的研究 (PROSPERO协议CRD42023492655) 的系统综述.
- 分析使用的模型 (例如,随机生存森林,DeepSurv),XAI技术 (例如,Shapley值) 和纳入的变量 (SDoH,性别).
- 专注于最近发表的研究 (大约. 2023年将达到一半) 和地理分布 (大多数来自美国).
主要成果:
- 深度学习 (DL) 模型显示,与机器学习 (ML) 模型相比,对心血管疾病结果的预测性能优越.
- 随机生存森林 (RSF) 和DeepSurv被确定为最有效的CVD结果预测模型.
- 观察到健康的社会决定因素 (SDoH) 和性别分层的有限纳入,只有五分之一的研究进行了性别分析.
结论:
- 深度学习生存模型比传统的机器学习模型提供了对心血管疾病结果的更高的预测准确性.
- 随机生存森林和DeepSurv目前是预测心血管疾病结果的领先模型.
- 未来的研究必须优先考虑人工智能模型的解释,包括健康和性别分层的社会决定因素,因为它们对心血管疾病有重大影响.
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