革命性的放射学分析:法语语言的未来 医疗保健中的自动语音识别
Mariem Jelassi1,2, Oumaima Jemai2,3, Jacques Demongeot4
1RIADI Laboratory, Ecole Nationale des Sciences de l'Informatique (ENSI), Manouba University, La Manouba 2010, Tunisia.
Diagnostics (Basel, Switzerland)
|May 11, 2024
概括
这项研究开发了法国用于放射学的自动语音识别 (ASR) 系统,实现了17.12%的单词错误率. 这种专门的ASR提高了法语医疗保健机构的医疗文档效率.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 放射学 放射学是一门学科.
背景情况:
- 放射学报告的准确转录对于患者护理和医疗文档至关重要.
- 现有的自动语音识别 (ASR) 系统经常与专门的医学术语和各种语言口音扎.
- 人们越来越认识到在特定的医学领域和语言中需要量身定制的ASR解决方案.
研究的目的:
- 为法国的放射学应用适应和评估微声大v2模型.
- 评估系统在转录医疗术语和各种法语口音方面的准确性.
- 证明专门的ASR在法语临床环境中提高效率的潜力.
主要方法:
- 为了适应,利用了Whisper Large-v2模型.
- 专注于法语语言的放射性音频数据.
- 涉及广泛的数据收集和医疗音频内容的预处理.
- 使用Word错误率 (WER) 评估性能.
主要成果:
- 在法语放射性转录中获得了17.121%的文字错误率 (WER).
- 证明了复杂的放射学数据和多样化的重音的有效转录.
- 验证了系统在专业医疗领域的能力.
结论:
- 适应的Whisper Large-v2模型提供了一个强大的工具,用于在法语中进行放射性转录.
- 这种专门的ASR系统可以显著提高医疗文档的效率.
- 未来的工作包括为特定的医疗专业和语言定制ASR,并与EHR集成.
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