在高级分析和人工智能模型中对心血管疾病风险和结果评估健康变量的社会决定因素:有针对性的审查
Jane L Snowdon1, Elisabeth L Scheufele1, Jill Pritts1
1Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142.
Ethnicity & disease
|June 7, 2024
概括
对心血管疾病 (CVD) 风险的先进预测模型往往忽视了种族和教育等社会因素. 包括这些变量可以提高准确性并促进公平的患者护理.
科学领域:
- 心血管疾病的研究研究.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 预测模型对于了解心血管疾病 (CVD) 风险和进展至关重要.
- 结合临床和社会特征,提供了对心血管疾病机制和环境影响的见解.
- 先进的分析,人工智能 (AI) 和机器学习 (ML) 越来越多地用于医疗保健.
研究的目的:
- 审查将相关的社会变量纳入心血管疾病的先进预测模型.
- 评估当前模型中可能影响患者护理,治疗和资源分配的潜在偏差.
主要方法:
- 针对2018-2019年间发表的研究进行了有针对性的文献综述.
- 评估了预测北美成年人心血管疾病风险,死亡率,存活率,并发症,进展和结果的研究.
- 在模型构建中包括社会变量得到了独立审查者的批判性评估.
主要成果:
- 在533个独特记录中,35项研究符合纳入标准,使用先进的统计和ML方法.
- 通常包含的变量是年龄 (97%),性别 (97%),并发症 (91%) 和行为风险因素 (80%).
- 种族/种族 (66%) 和教育 (9%) 等社会变量较少被纳入.
结论:
- 对心血管疾病风险的预测模型需要对种族和社会变量进行调整以提高准确性.
- 纳入这些因素对于在患者管理中制定更公平的干预措施和决策过程至关重要.
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