人工智能模型用于识别高风险的患者对玻璃眼使用自我报告的健康数据在美国国家队列中的患者
Rohith Ravindranath1, Joel Naor1, Sophia Y Wang1
1Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Ophthalmology science
|March 28, 2025
概括
人工智能模型可以使用自我报告的健康数据来预测眼风险. 这使得早期检测和有针对性的查成为可能,特别是在资源较低的环境中.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 早期发现青光眼对于预防视力丧失至关重要.
- 目前的选方法需要专门的设备,限制了广泛实施.
- 开发可访问的预选工具对于早期干预至关重要.
研究的目的:
- 开发人工智能 (AI) 模型,用于对玻璃眼风险进行预先查.
- 为了风险预测,利用调查中的自我报告的健康数据.
- 识别需要进一步眼科检查和成像的高风险个体.
主要方法:
- 一项涉及我们所有人研究计划 (2018-2022) 参与者的队列研究.
- 开发人工智能模型 (逻辑回归,XGBoost,神经网络) 使用自我报告的人口统计,健康,生活方式和病史.
- 通过来自电子健康记录 (EHR) 的疾病国际分类代码识别眼的诊断.
主要成果:
- 人工智能模型在从调查数据中预测眼风险方面取得了很高的准确性.
- XGBoost模型展示了最高的预测性能,AUROC为0.890.
- 关键的预测特征包括年龄,2型糖尿病和家族眼病史.
结论:
- 机器和深度学习模型有效地使用自我报告的健康数据预测玻璃眼.
- 这些人工智能工具可以在多样化,低资源的环境中促进玻璃眼前选.
- 通过这些模型识别的高风险个体可以优先进行专门的眼科检查.
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