Tutorial: Neuro-symbolic AI for Mental Healthcare
Loading...
Links to Files
Permanent Link
Author/Creator
Author/Creator ORCID
Date
2022-09-19
Type of Work
Department
Program
Citation of Original Publication
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Subjects
Abstract
Artificial Intelligence (AI) systems for mental healthcare (MHCare)
have been ever-growing after realizing the importance of early
interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for
supporting patients seeking MHCare. The creation of peer-support
groups without social stigma has resulted in patients transitioning
from clinical settings to SocMedia supported interactions for quick
help. Researchers started exploring SocMedia content in search
of cues that showcase correlation or causation between different
MH conditions to design better interventional strategies. User-level
Classification-based AI systems were designed to leverage diverse
SocMedia data from various MH conditions, to predict MH conditions. Subsequently, researchers created classification schemes to
measure the severity of each MH condition. Such ad-hoc schemes,
engineered features, and models not only require a large amount of
data but fail to allow clinically acceptable and explainable reasoning
over the outcomes. To improve Neural-AI for MHCare, infusion of
clinical symbolic knowledge that clinicans use in decision making is required. An impactful use case of Neural-AI systems in
MH is conversational systems. These systems require coordination
between classification and generation to facilitate humanistic conversation in conversational agents (CA). Current CAs with deep
language models lack factual correctness, medical relevance, and
safety in their generations, which intertwine with unexplainable
statistical classification techniques. This lecture-style tutorial will
demonstrate our investigations into Neuro-symbolic methods of
infusing clinical knowledge to improve the outcomes of Neural-AI
systems to improve interventions for MHCare:(a) We will discuss
the use of diverse clinical knowledge in creating specialized datasets
to train Neural-AI systems effectively. (b) Patients with cardiovascular disease express MH symptoms differently based on gender
differences. We will show that knowledge-infused Neural-AI systems can identify gender-specific MH symptoms in such patients.
(c) We will describe strategies for infusing clinical process knowledge as heuristics and constraints to improve language models in
generating relevant questions and responses.