Process Knowledge-infused Learning for Clinician-friendly Explanations
Kaushik Roy Yuxin Zi Manas Gaur Jinendra Malekar Qi Zhang Vignesh Narayanan Amit Sheth
Language models have the potential to assess mental health using social media
data. By analyzing online posts and conversations, these models can detect
patterns indicating mental health conditions like depression, anxiety, or
suicidal thoughts. They examine keywords, language markers, and sentiment to
gain insights into an individual's mental well-being. This information is
crucial for early detection, intervention, and support, improving mental health
care and prevention strategies. However, using language models for mental
health assessments from social media has two limitations: (1) They do not
compare posts against clinicians' diagnostic processes, and (2) It's
challenging to explain language model outputs using concepts that the clinician
can understand, i.e., clinician-friendly explanations. In this study, we
introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm
that layers clinical process knowledge structures on language model outputs,
enabling clinician-friendly explanations of the underlying language model
predictions. We rigorously test our methods on existing benchmark datasets,
augmented with such clinical process knowledge, and release a new dataset for
assessing suicidality. PK-iL performs competitively, achieving a 70% agreement
with users, while other XAI methods only achieve 47% agreement (average
inter-rater agreement of 0.72). Our evaluations demonstrate that PK-iL
effectively explains model predictions to clinicians.