Automated Empathy Detection for Oncology Encounters
This work addresses the need for efficient empathy analysis in oncology to support patient-centered care, but it is incremental as it applies existing methods like SVMs to a new domain.
The paper tackled the problem of high-cost manual annotation for empathy detection in clinical oncology encounters by proposing a multimodal system that automatically detects empathic interactions using speech and language processing, achieving performance evaluated in terms of average precision.
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).