Process Knowledge-Infused Learning for Clinician-Friendly Explanations

Author(s)

Kaushik Roy, University of South Carolina - ColumbiaFollow
Yuxin Zi, University of South Carolina - ColumbiaFollow
Manas Gaur
Jinendra Malekar, University of South Carolina - ColumbiaFollow
Qi Zhang, University of South Carolina - ColumbiaFollow
Vignesh Narayanan, University of South Carolina - ColumbiaFollow
Amit Sheth, University of South Carolina - ColumbiaFollow

Document Type

Conference Proceeding

Abstract

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. PKiL 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.

Digital Object Identifier (DOI)

https://doi.org/10.1609/aaaiss.v1i1.27494

Publication Info

Published in Proceedings of the Inaugural 2023 Summer Symposium Series 2023, Volume 1, Issue 1, 2023.

Copyright © 2023, Association for the Advancement of Artificial Intelligence.

APA Citation

Roy, K., Zi, Y., Gaur, M., Malekar, J., Zhang, Q., Narayanan, V., & Sheth, A. (2023). Process knowledge-infused learning for clinician-friendly explanations. Proceedings of the AAAI Symposium Series, 1(1), Article 1. https://doi.org/10.1609/aaaiss.v1i1.27494