Empathy involves understanding other people's situation, perspective, and feelings. In clinical interactions, it helps clinicians establish rapport with a patient and support patient-centered care and decision making. Understanding physician communication through observation of audio-recorded encounters is largely carried out with manual annotation and analysis. However, manual annotation has a prohibitively high cost. In this paper, a multimodal system is proposed for the first time to automatically detect empathic interactions in recordings of real-world face-to-face oncology encounters that might accelerate manual processes. An automatic speech and language processing pipeline is employed to segment and diarize the audio as well as for transcription of speech into text. Lexical and acoustic features are derived to help detect both empathic opportunities offered by the patient, and the expressed empathy by the oncologist. We make the empathy predictions using Support Vector Machines (SVMs) and evaluate the performance on different combinations of features in terms of average precision (AP).

中文翻译：

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肿瘤学遭遇的自动移情检测

同理心包括理解他人的处境、观点和感受。在临床互动中，它帮助临床医生与患者建立融洽的关系，并支持以患者为中心的护理和决策。通过观察音频记录的遭遇来了解医生的交流主要是通过手动注释和分析来进行的。然而，手动注释的成本高得令人望而却步。在本文中，首次提出了一种多模式系统，以自动检测现实世界面对面肿瘤学遭遇记录中的共情交互，这可能会加速手动过程。使用自动语音和语言处理管道对音频进行分割和分类，以及将语音转录为文本。导出词汇和声学特征以帮助检测患者提供的移情机会和肿瘤学家表达的移情。我们使用支持向量机 (SVM) 进行移情预测，并根据平均精度 (AP) 评估不同特征组合的性能。