早期检测急性冠状动脉综合征使用移动数字健康应用程序
Mifetika Lukitasari1, Allen Lamarca Nazareno2, Mohammad Saifur Rohman3,4
1School of Population Health, UNSW Sydney, NSW 2052 Australia.
Studies in health technology and informatics
|August 8, 2025
概括
早期发现急性冠状动脉综合征 (ACS) 是至关重要的. 使用机器学习的DETAK应用程序问卷准确预测了ACS,显示了全社区早期诊断的潜力.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 数字健康数字健康
背景情况:
- 早期发现急性冠状动脉综合征 (ACS) 对于尽量减少心肌损伤至关重要.
- 胸部疼痛是需要快速评估的主要症状.
- 移动健康应用程序为及时诊断工具提供了新的途径.
研究的目的:
- 评估基于移动应用程序的问卷对预测ACS的有效性.
- 评估随机森林机器学习模型在识别 ACS 病例中的性能.
- 确定DETAK应用程序在广泛早期ACS检测方面的潜力.
主要方法:
- 在专家共识下开发的胸痛评估问卷被整合到DETAK移动应用程序中.
- 分析了566名患者的数据 (412名患有ACS,154名没有).
- 一个随机森林机器学习模型 (Python 3.12.4) 用于ACS预测.
主要成果:
- 机器学习模型实现了高性能指标:准确度为0.81,精度为0.86,回忆力为0.9,F1得分为0.88.
- 该模型显示ACS预测的特异性为0.54.
- DETAK问卷提供了一个快速有效的方法,用于初始ACS评估.
结论:
- 移动应用程序DETAK的胸痛问卷,再加上机器学习,显示了急性冠状动脉综合征早期检测的巨大潜力.
- 这个数字健康工具可以促进更广泛的社区查和ACS的及时干预.
- 进一步实施这些工具可以通过减少缺血时间来改善患者的治疗结果.
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