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

Automated Empathy Detection for Oncology Encounters