自动化静脉血栓栓塞风险评估:一种使用电子病历的双分支深度学习方法
Jianhua Yang1, Jianfeng He1, Hongjiang Zhang2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
Frontiers in medicine
|August 28, 2023
概括
本研究引入了一种双分支深度学习方法,用于自动静脉血栓塞栓症 (VTE) 风险评估. 人工智能模型有效地分析电子病历,大大改善了手动VTE风险评估.
科学领域:
- 心血管医学 心血管医学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 静脉血栓栓塞 (VTE) 是一种常见的心血管疾病.
- 手动VTE风险评估是低效的,并且覆盖了有限的患者群体.
- 有效的VTE风险评估需要智能方法.
研究的目的:
- 开发一种使用深度学习进行VTE风险评估的自动化方法.
- 提高VTE风险评估的效率和人口覆盖率.
- 为了解决手动帕多瓦尺度评估的局限性.
主要方法:
- 提出了一种双分支深度学习 (DB-DL) 模型,其中包含了帕多瓦疾病分类模型 (PDCM).
- PDCM分析了疾病类别判断的诊断,症状和权重.
- 中国词汇分析 (LAC) 和基于规则的系统从电子病历 (EMR) 中提取临床因素.
主要成果:
- 通过DB-DL方法实现了自动化静脉瘤风险评估,平均评估时间为每名患者0.37秒.
- 对7690个中国临床EMR的验证显示曲线下的面积 (AUC) 为0.883.
- 与黄金标准相比,该模型表现出高特异性 (0.957) 和灵敏度 (0.816).
结论:
- DB-DL评估方法自动化了VTE风险评估,克服了时间和覆盖范围的限制.
- 这种人工智能驱动的方法为VTE风险管理提供了重要的临床价值.
- 这项研究强调了深度学习在提高心血管疾病风险评估方面的潜力.
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