# Automated Empathy Detection for Oncology Encounters

arXiv: https://arxiv.org/abs/2007.00809 · HTML: https://mlsanity.com/paper/2007.00809/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods](https://mlsanity.com/paper/2311.00721/) (2023)
- [An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates](https://mlsanity.com/paper/2106.07922/) (2021)
- [Turn-Level Empathy Prediction Using Psychological Indicators](https://mlsanity.com/paper/2407.08607/) (2024)

## Abstract (arXiv)

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).