TrajVis:一种视觉临床决策支持系统,用于将人工智能轨迹模型转化为慢性病的精确管理
Zuotian Li1,2, Xiang Liu1, Ziyang Tang3
1Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
概括
TrajVis是一个新的工具,可以帮助医生使用人工智能来管理慢性疾病,通过可视化来自电子病历 (EMR) 的患者数据. 它有助于个性化护理和疾病进展跟踪.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据可视化 数据可视化
背景情况:
- 慢性疾病需要个性化的管理策略.
- 电子医疗记录 (EMR) 包含有价值的纵向患者数据.
- 将人工智能 (AI) 整合到慢性疾病管理的临床实践中是一项挑战.
研究的目的:
- 为临床医生开发和验证TrajVis,一个互动工具.
- 允许使用AI模型与EMR用于慢性疾病的精确管理.
- 帮助理解和预测慢性疾病的进展.
主要方法:
- 与临床医生和数据科学家一起进行需求分析.
- 使用图形AI模型 (DEPOT) 开发TrajVis用于慢性病 (CKD) 轨迹推断.
- 实现作为使用合成EMR数据的完整堆Web应用程序.
- 通过与科医生的案例研究和用户体验调查进行评估.
主要成果:
- TrajVis的功能包括患者视图,轨迹视图,临床指标视图和分析视图.
- 该系统可视化CKD轨迹并解释AI预测.
- 评估表明,TrajVis有助于总结数据,识别风险预测因素,并可视化疾病进展.
- 该工具解决了在医疗保健中实施AI的障碍.
结论:
- TrajVis提供了一种新的可视化解决方案,补充了现有的风险估计器.
- 它弥合了AI/机器学习 (ML) 建模和临床应用之间的差距.
- 该系统促进了慢性疾病的个性化和精确管理.
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