预测患糖尿病病风险的机器学习技术:一篇文献综述
F Mesquita1, J Bernardino1,2, J Henriques2
1Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes - Quinta da Nora, 3030-199 Coimbra, Portugal.
Journal of diabetes and metabolic disorders
|June 27, 2024
概括
机器学习模型可以通过利用临床数据中的时间因素来改善糖尿病病 (DN) 的预测. 利用电子健康记录 (EHR) 和omics数据可以提高这种糖尿病并发症的预测准确度.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 科研究 科研究
背景情况:
- 糖尿病是一种全球性的健康问题.
- 糖尿病病 (DN) 是一种严重的并发症,逐渐损害功能.
- 早期和准确的DN预测对于患者管理至关重要.
研究的目的:
- 评估机器学习 (ML) 模型在预测糖尿病病 (DN) 风险方面的有效性.
- 评估ML模型是否可以利用临床数据的时间维度来改善DN预测.
- 将基于ML的预测与现有的临床模型进行比较.
主要方法:
- 进行了全面的文献审查.
- 搜索的数据库包括Scopus,科学网络和PubMed.
- 包括2015年1月至2022年12月期间发表的英语文章.
主要成果:
- 分析了11项研究,重点关注临床数据的ML算法.
- 在不同研究中,患者数据中的时间因子被不一致地使用.
- 不同的ML方法的性能,可解释性和优点/缺点进行了比较.
结论:
- 利用电子健康记录 (EHR) 数据的时间方面可以增强DN预测模型.
- 将omics数据与时间EHR数据集成,有可能产生更强大的预测工具.
- 进一步的研究应侧重于最大限度地利用时间数据进行DN风险评估.
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