用户对重症患者的语音/短语识别软件的评估:DECIDE-AI可行性研究
M Musalia1, S Laha2,3, J Cazalilla-Chica1
1Critical Care Unit, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK.
Critical care (London, England)
|July 10, 2023
概括
语音/短语识别软件对于言语障碍重症患者来说是有前途的. 深度神经网络实现了75%的排名1准确度,提供了一个新的通信辅助工具.
科学领域:
- 医学技术 医学技术 医学技术
- 医疗保健中的人工智能
- 关键护理医学 关键护理医学
背景情况:
- 危急病患者通常会出现语言障碍,需要使用视觉图表或手势等替代性沟通方法.
- 现有的沟通辅助工具可能无法完全恢复严重语言障碍患者的自然互动.
研究的目的:
- 评估一种新型语音/短语识别应用程序在严重病患的语音障碍患者中的有效性.
- 评估动态时间扭曲 (DTW) 和深度神经网络 (DNN) 的性能,以识别口语.
主要方法:
- 一项前性研究是在第三级医院的重症监护病房进行的.
- 14名气管切除术患者使用了语音障碍者语音/短语识别应用程序 (SRAVI) 来口语短语.
- 用DNN和DTW方法分析记录,结果按可能性排序.
主要成果:
- 通过DNN方法实现了整体识别准确率为86%,排名1准确率为75%.
- DTW方法的总识别准确率为74%,排名1准确率为48%.
- 在616个 (83.8%) 记录的短语中,516个是可识别的.
结论:
- 语音/短语识别软件,特别是使用DNN,在帮助重症患者沟通方面证明了可行性和有效性.
- 这项技术有可能显著改善语言障碍患者的沟通.
- 进一步的研究和开发可以加强其作为重症监护机构治疗选择的作用.
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