Empathy is a vital component of health care and plays  a key role in the training of future doctors. Paying attention to medical students' self-reflective stories of their interactions with patients can encourage empathy and the formation of professional identities that embody desirable values such as integrity and respect. We present a computational approach and linguistic analysis of empathic language in a large corpus of 440 essays written by pre-med students as narrated simulated patient -- doctor interactions. We analyze the discourse of three kinds of empathy: cognitive, affective, and prosocial as highlighted by expert annotators. We also present various experiments with state-of-the-art recurrent neural networks and transformer models for classifying these forms of empathy. To further improve over these results, we develop a novel system architecture that makes use of frame semantics to enrich our state-of-the-art models. We show that this novel framework leads to significant improvement on the empathy classification task for this dataset.

Enriching Deep Learning with Frame Semantics for Empathy Classification in Medical Narrative Essays

Priyanka Dey

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