这是病人说话还是护士? 在患者-护士录音中自动识别扬声器类型
Maryam Zolnoori1,2, Sasha Vergez2, Sridevi Sridharan2
1School of Nursing, Columbia University, New York, New York, USA.
概括
这项研究开发了机器学习模型,以区分患者和护士的言语,识别语言模式. 这些模型可以帮助分析沟通,以早期发现疾病并改善患者的治疗结果.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的机器学习
- 临床沟通分析 临床沟通分析
背景情况:
- 患者与临床医生的沟通是健康信息的丰富来源.
- 分析这种沟通以获得临床见解是一个尚未探索的领域.
- 开发语音分析的自动化方法对于提取有价值的数据至关重要.
研究的目的:
- 在音频录制的互动中分析患者和护士的语言模式.
- 开发机器学习 (ML) 分类器,以区分患者和护士的语言.
- 建立一个基础,用于分析患者的言语,以早期识别风险.
主要方法:
- 采集和转录了录制的音频记录患者与护士的互动.
- 使用自然语言处理 (NLP) 技术来提取语言特征.
- 机器学习分类器,包括支持向量机器,被训练来区分扬声器.
主要成果:
- 一个支持向量机器分类器实现了高性能 (AUC-ROC 99.01%,F1得分96.82%).
- 患者倾向于使用非正式语言和与个人生活相关的关键词 (例如,宗教,家庭,金钱).
- 护士使用了更复杂的句子,专注于医疗主题,并提出了更多的问题.
结论:
- 开发的方法有效地区分了患者和护士的语言.
- 这种方法是分析患者言语以确定疾病风险的关键前体.
- 临床沟通的自动化分析有可能改善患者的护理和结果.
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