开发和验证自然语言处理系统以评估前列腺癌咨询中的医生沟通质量
Renning Zheng1,2, Nadine A Friedrich1, Michael Luu3
1Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Prostate cancer and prostatic diseases
|August 22, 2025
概括
自然语言处理 (NLP) 模型可以准确地评估前列腺癌咨询中的医生沟通质量. 这些模型有助于确保患者获得关键信息,以便就治疗选择进行共享决策.
科学领域:
- 尿道病学
- 医疗信息学
- 关于健康的信息
背景情况:
- 美国前列腺协会的指导方针强调了前列腺癌护理的共同决策 (SDM),要求讨论瘤风险,预期寿命,预后,基线功能和治疗副作用.
- 患者信息的保留和不一致的医生风险沟通构成了有效的SDM的障碍.
- 自然语言处理 (NLP) 为改善信息传递和沟通质量评估提供了一个潜在的解决方案.
研究的目的:
- 在前列腺癌咨询中开发和验证NLP模型以识别关键信息.
- 评估这些NLP模型对特定内容领域的医生沟通质量进行审核的能力.
主要方法:
- 在50份咨询成绩单上训练并验证了NLP模型,以确定与基本SDM概念相关的句子.
- 在20次单独的咨询中,使用顶部识别的句子来评分沟通质量的模型性能.
主要成果:
- 随机森林NLP模型在识别主题一致的句子方面表现出高精度 (大多数概念的AUC> 0.80).
- 通过NLP模型识别的最高句子在整个咨询中准确地分类了沟通质量,不同概念的准确度从80%到100%不等.
结论:
- 在前列腺癌咨询中,NLP模型有效地捕获关键信息并评估医生的沟通质量.
- 这些模型为评估风险沟通和支持患者明智决策提供了可扩展的基础.
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