English(EN) Augmenting Interviewer Judgments of Patient Experience with Automatic Language Analysis

AI语言分析增强临床医生对患者体验的判断

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一个框架，通过结合人类面试官的判断和自动语言分析来改进临床访谈中对患者体验的估计。该方法使用包括BiLSTM在内的各种机器学习模型，并在对话记录上进行训练。与单独使用任一来源相比，结合面试官评分和模型预测的综合方法在近似患者报告的互动质量方面表现出更优越的性能。 AI

影响这项研究表明，AI可以在临床环境中为人类判断提供补充见解，从而可能改善患者护理反馈。

排序理由该集群包含一篇详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Aowen Shi, Michal Balazia, Danilo Postin, Ren\'e Hurlemann, Jan Alexandersson, Fran\c{c}ois Br\'emond, Philipp M\"uller · 2026-09-01 04:00

利用自动语言分析增强面试官对患者体验的判断

arXiv:2608.31007v1 Announce Type: cross Abstract: Understanding how psychiatric patients subjectively experienced a clinical conversation is important for feedback and alliance-related process monitoring. While interviewers form post-session judgments about patient experience, th…