Demo alleviate: demonstrating artificial intelligence enabled virtual assistance for telehealth: the mental health case
AUTHORs: 
Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, 
Amit ShethAuthors Info & Claims
Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit ShethAuthors Info & ClaimsArticle No.: 2016, Pages 16479 - 16481
Abstract
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care 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 design and explainable decision-making lends itself to robust and continued feedback-based refinements to its design. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better to facilitate optimal care strategies.
References
[1]
Cameron, D.; Kavuluru, R.; Rindflesch, T. C.; Sheth, A. P.; Thirunarayan, K.; and Bodenreider, 0.2015. Context-driven automatic subgraph creation for literature-based discovery. Journal of biomedical informatics, 54: 141-157.
[2]
Gupta, S.; Agarwal, A.; Gaur, M.; Roy, K.; Narayanan, V.; Kumaraguru, P.; and Sheth, A. 2022. Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, 137-147. Seattle, USA: Association for Computational Linguistics.
[3]
Jaimini, U.; Thirunarayan, K.; Kalra, M.; Venkataraman, R.; Kadariya, D.; Sheth, A.; et al. 2018. "How Is My Child's Asthma?" digital phenotype and actionable insights for pediatric asthma. JMIR pediatrics and parenting, 1(2): e11988.
[4]
Rawte, V.; Chakraborty, M.; Roy, K.; Gaur, M.; Faldu, K.; Kikani, P.; Akbari, H.; and Sheth, A. P. 2022. TDLR: Top Semantic-Down Syntactic Language Representation. In NeurIPS '22 Workshop on All Things Attention: Bridging Different Perspectives on Attention.
[5]
Roy, K.; Gaur, M.; Rawte, V.; Kalyan, A.; and Sheth, A. 2022a. ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance. arXiv preprint arXiv:2204.12560.
[6]
Roy, K.; Gaur, M.; Zhang, Q.; and Sheth, A. 2022b. Process Knowledge-infused Learning for Suicidality Assessment on Social Media. arXiv preprint arXiv:2204.12560.
[7]
Roy, K.; Lokala, U.; Khandelwal, V.; and Sheth, A. 2021a. "Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision. Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), 2846.
[8]
Roy, K.; Zhang, Q.; Gaur, M.; and Sheth, A. 2021b. Knowledge infused policy gradients for adaptive pandemic control. In CEUR Workshop Proc.
[9]
Roy, K.; Zhang, Q.; Gaur, M.; and Sheth, A. 2021c. Knowledge infused policy gradients with upper confidence bound for relational bandits. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 35-50. Springer.
[10]
Sheth, A.; Gaur, M.; Roy, K.; Venkataraman, R.; and Khandelwal, V. 2022. Process Knowledge-Infused AI: Toward User-Level Explainability, Interpretability, and Safety. IEEE Internet Computing, 26(5): 76-84.
[11]
WCVB. 2020. 40% of US adults reported struggling with mental health or substance abuse, according to June CDC survey. http://bit.ly/news_mental_health. Accessed: 202212-10.
[12]
WHO. 2022. Covid-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide. https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide. (Accessed on 11/01/2022).
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Copyright © 2023 Association for the Advancement of Artificial Intelligence.
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Published: 07 February 2023
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