强大的诊断推系统,用于初级保健远程医疗使用长期短期记忆多类序列分类
Patrick Essay1, Ajaykumar Rajasekharan1
1Teladoc Health, Inc, 1875 Lawrence St, Denver, CO, 80202, USA.
Heliyon
|March 21, 2024
概括
这项研究开发了一个深度学习模型,为远程医疗提供者推诊断,提高选择适当的ICD-10代码的效率和准确性. 该系统通过在平台内提供更快,更相关的诊断建议来支持临床医生.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 深度学习应用程序
背景情况:
- 远程医疗平台可以增强医疗保健提供者的诊断编码支持.
- 在虚拟咨询中,有效的诊断选择对于及时的患者护理至关重要.
研究的目的:
- 开发一种基于深度学习的推系统,用于国际疾病分类第10版 (ICD-10) 编码.
- 帮助远程医疗临床医生更快,更准确地进行诊断选择.
主要方法:
- 使用长短期记忆 (LSTM) 模型进行多类序列分类.
- 输入功能包括患者的症状,投诉和咨询请求的原因.
- 在通用医学,皮肤病学和心理健康领域的280多万次远程医疗咨询数据上训练了模型.
主要成果:
- 在一般医学,皮肤病学和心理健康专业中,LSTM推器的平均准确率为31.7%.
- 在前20个推诊断中,平均覆盖率为85.8%.
- 实现了0.87的平均个性化得分,表明了量身定制的建议.
结论:
- 通过LSTM多类序列分类,有效地推针对个人远程医疗咨询的诊断.
- 该系统是可重组的,可以减少供应商花费寻找诊断代码的时间和资源.
- 推者在各种临床专业中表现出强度,增强诊断工作流程.
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