在英国生物银行的心血管疾病风险预测模型开发中,对以模型为中心和以数据为中心的方法进行了比较研究
Mohammad Mamouei1,2, Thomas Fisher1,2, Shishir Rao1,2
1Deep Medicine, Oxford Martin School, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK.
European heart journal. Digital health
|August 4, 2023
概括
通过使用全面的病史和先进的机器学习模型,预测心血管疾病 (CVD) 风险得到了改进. 纳入社会经济因素和测量也提高了预测准确性,以获得更好的公共卫生结果.
科学领域:
- 计算生物学和生物信息学
- 流行病学和公共卫生.
- 机器学习在医疗保健中的应用
背景情况:
- 心血管疾病 (CVD) 受许多因素的影响,但它们的相互作用和预测贡献尚未完全理解.
- 现有的风险预测模型往往缺乏捕捉医疗事件时间性质的能力.
- 需要更全面的方法来预测心血管疾病发病率.
研究的目的:
- 调查各种风险因素类别对心脏病发病率预测的贡献.
- 评估机器学习和深度学习模型的性能与现有的临床风险评分相比.
- 探索综合病史和新型建模技术对预测准确性的影响.
主要方法:
- 利用英国生物库,一个大规模的数据集,包括405,257名参与者 (年龄在37-73岁之间).
- 训练有素的机器学习和深度学习模型 (包括BEHRT) 对各种风险因素的子集 (病史,行为,社会经济,环境,测量).
- 与已建立的QRISK3风险评分对比的基准模型性能.
主要成果:
- 综合病史显著改善了心血管疾病发病率预测模型,在歧视 (3.78%) 和精度 (1.80%) 中超过了QRISK3.
- 神经序列模型BEHRT展示了详细病史的增强效益,突出了事件时间性的重要性.
- 社会经济因素和测量为预测绩效提供了小但重要的独立贡献.
结论:
- 整合健康的广泛决定因素和采用先进的建模方法对于提高心血管疾病发病率预测至关重要.
- 像BEHRT这样的模型所捕捉到的医疗事件的暂时性,与传统的风险评估相比,提供了显著的优势.
- 未来的CVD风险预测策略应该包含全面的数据和复杂的分析技术.
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