机器学习框架的增量设计用于医疗记录处理.
Christopher Streiffer1, Divya Saini1, Gideon Whitehead2
1Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
概括
在使用机器学习的诊所中,coordin8应用程序显著减少了传真处理时间. 这种人为循环系统提高了患者识别和文档分类等任务的效率和准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床工作流的优化 临床工作流优化
背景情况:
- 门诊诊所的传真处理是耗时和低效的.
- 自动化临床管理任务对于改善医疗保健服务至关重要.
- 现有的解决方案往往缺乏适应不同临床环境所需的适应性.
研究的目的:
- 开发和评估coordinn8,一个基于Web的应用程序,用于简化门诊诊所的传真处理.
- 为实现自动化传真分析的人在循环中的机器学习框架.
- 评估coordinn8对处理时间和机器学习推断精度的影响.
主要方法:
- 在11个门诊部署了coordinn8.
- 使用用户观察和传真处理事件日志进行时间节省分析.
- 机器学习模型概括性和性能的统计评估.
- 时间序列分析,以监测和减轻随着新诊所上线的模型漂移.
主要成果:
- 单个传真处理时间平均减少147.5秒.
- 机器学习任务的高精度:81.6%的文档分类,83.7%的患者识别,98.4%的垃圾邮件分类,81.0%的重复检测精度.
- 在重新培训后,文件分类准确度提高了10.2%.
结论:
- coordn8显著减少了传真处理时间,并提供了准确的机器学习推理.
- 人在循环框架有效地收集高质量的数据用于模型培训.
- 模型再培训成功地缓解了与扩展到新诊所相关的性能下降.
- 开发的框架可以作为卫生系统实施类似人工智能技术的模板.
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