走向基于人工智能的疾病预测算法,全面利用并从现实世界的临床表格数据系统中不断学习
Terrence J Lee-St John1, Oshin Kanwar1, Emna Abidi1
1Research Department, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates.
PLOS digital health
|September 3, 2024
概括
一个新的算法通过整合人工智能和统计数据来估计电子健康记录 (EHR) 的疾病风险. 这种自适应系统可以随着时间的推移而改善预测,而无需手动处理数据,对中风和心肌梗塞风险具有很高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床流行病学临床流行病学
背景情况:
- 现实世界的临床数据,如电子健康记录 (EHR),包含了大量用于疾病风险预测的信息.
- 传统方法通常依赖于精心策划的数据集和预定义的风险因素,限制了适应性和范围.
- 从原始临床数据开发动态的,自动化的疾病风险计算器仍然是一个挑战.
研究的目的:
- 通过使用现实世界的临床表格数据来估计疾病风险的可通用,自动化策略 (完整的算法) 的概念证明.
- 开发一个自我适应的预测系统,它随着新收集的数据而发展.
- 为了证明利用全面的真实世界数据来准确预测疾病风险而不需要广泛的先验治疗的可行性.
主要方法:
- 完整的算法集成了统计方法和人工智能来分析EHR数据,识别预测变量,将数据结构化为时间序列,并训练神经网络预测模型.
- 该系统被设计为自我适应,随着新数据的可用性,自动更新预测机制.
- 使用EHR数据进行了伪前性验证,以估计初始中风或心肌梗塞的12个月风险.
主要成果:
- 该算法实现了接收器运行特征曲线 (AUROC) 下的区域值,用于预测中风或心肌梗塞风险,从0.830到0.909不等,随着时间的推移趋势有所改善.
- 用几率比率表示的模型精度,在高风险患者群体 (1-100和101-200) 中表现强,值在7.2至48.1之间,也显示出改善趋势.
- 该验证涉及558,105名患者和3,424,060名患者月的数据,从2015年4月到2023年9月.
结论:
- 提出的自动化策略是开发高性能,自适应性疾病风险计算器的可行方法,使用现实世界的临床数据.
- 该算法有效地整合了来自EHR的各种数据类型,以捕捉复杂的疾病动态.
- 这种方法提供了一个强大的替代传统的静态模型,使得更准确和响应风险估计.
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