Mental Health Projects

Mental Health Projects at the AIISC

The world, after the peak effects of the COVID-19 pandemic, has renewed interest in the challenges faced by mental health care services required. The increased needed care is due to significant after-effects from lockdown isolations, economic hardships, grief, and fear. At the AIISC, we are pioneering research efforts to assist care providers and care seekers in meeting their healthcare needs through AI-powered assistive technology. The salient features of the technology we develop are:

(1) Safety constrained AI outcomes - Ensuring that the AI maintains clinically accepted safety standards and incorporates mechanisms for involving the clinician when uncertain.

(2) Modular and explainable Algorithms, allowing for robust human-understandable system evaluation as per clinical standards with our clinician partners

(3) Rigorous Usability testing using state-of-the-art evaluation standards and metrics.

Our efforts towards this end include the following projects:

Diagnostic assistance to Support Providers through Web Services

Process Knowledge-infused Learning (PKiL) that uses AI techniques performing web-scale annotation helpful for Mental Health Diagnostic Assistance. PKiL annotations are grounded in established diagnosis processes in active use during clinical practice. Consequently, PKiL ensures that strict medical standards are maintained with regard to safety of the service and the user-understandable explainability of outcomes.

Read related papers here:

(1) Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case

(2) ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance

(3) Process Knowledge-infused Learning for Suicidality Assessment on Social Media

(4) Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts

Bringing Support Seekers and Support Providers Together through Web Services

Subreddits on Reddit, such as r/Coronavirus, provide valuable insights into user needs for help (support seekers- SSs) and the appropriate available service from individuals with relevant professional experiences and perspectives on care (support providers - SPs). Knowledgeable human moderators match an SS with an SP with relevant experience on these subreddits, reflected through self-explanatory annotations. We leverage the moderator’s annotations to develop knowledge-infused learning techniques to capture the thinking process that a moderator uses to match a SS to an SP. The match categories are supportive, informative, or similar (sharing experiences). Evaluation by 21 domain experts shows the efficacy of the matching system.

Read the related paper here:: "Who can help me?" Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

Artificial Intelligence Enabled Virtual Assistance for Mental Health Telehealth (ALLEVIATE)

AI-enabled telehealth for adequate mental health care involves significant challenges. The breadth and complexity of the challenges involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In addition, ALLEVIATE’s modular designs and explainable decision-making lend themselves to robust and continued feedback-based refinements to its design. ALLEVIATE is an essential step toward helping patients and clinicians understand each other better to facilitate optimal care strategies.

References

• Kaushik Roy, Yuxin Zi, Manas Gaur, Jinendra Malekar, Qi Zhang, Vignesh Narayanan, Amit Sheth. (June 16. 2023) Process Knowledge-infused Learning for Clinician-friendly Explanations

• Gaur Manas, Vamsi Aribandi, Ugur Kursuncu, Amanuel Alambo, Valerie L Shalin, Krishnaprasad Thirunarayan, Jonathan Beich, Meera Narasimhan, Amit Sheth. (May 30, 2020) Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study

• Roy, K., Khandelwal, V., Goswami, R., Dolbir, N., Malekar, J., & Sheth, A. (2023). Demo Alleviate: Demonstrating artificial intelligence enabled virtual assistance for telehealth: The mental health case. Thirty-Seventh AAAI Conference on Artificial Intelligence.

• Roy, K., Gaur, M., Rawte, V., Kalyan, A., & Sheth, A. (2023). ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance., Frontiers Big Data, 09 January 2023

• Gaur, M., Kursuncu, U., Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018, October). " Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 753-762).

• Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth. Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts.
  

• Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth. Process Knowledge-infused Learning for Suicidality Assessment on Social Media.
  

• Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth. Process Knowledge-infused Learning for Suicidality Assessment on Social Media.
  

• Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata. Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts in Joint European Conference on Machine Learning and Knowledge Discovery in Databases.
  

• Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth. Knowledge infused policy gradients with upper confidence bound for relational bandits.
  

• Amit Sheth, Manas Gaur, Kaushik Roy, Keyur Faldu. Knowledge-intensive language understanding for explainable AI. IEEE Internet Computing.
  

• Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth. “Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit. 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)
  

• Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit Sheth. L" Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision.
  

• Amit Sheth, Kaushik Roy, Manas Gaur, Usha Lokala. Tutorial on Knowledge In-Wisdom Out-Explainable Data for AI in Cyber Social Threats and Public Health.