# Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case

arXiv: https://arxiv.org/abs/2304.00025 · HTML: https://mlsanity.com/paper/2304.00025/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [Process Knowledge-infused Learning for Suicidality Assessment on Social Media](https://mlsanity.com/paper/2204.12560/) (2022)
- ["Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision](https://mlsanity.com/paper/2102.01222/) (2021)
- ["Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit](https://mlsanity.com/paper/2105.06398/) (2021)
- [Knowledge Infused Policy Gradients for Adaptive Pandemic Control](https://mlsanity.com/paper/2102.06245/) (2021)
- [Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits](https://mlsanity.com/paper/2106.13895/) (2021)

## Built on by (most cited first, 4 of 4)

- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19](https://mlsanity.com/paper/2411.07163/) (2024)
- [Knowledge-Infused Self Attention Transformers](https://mlsanity.com/paper/2306.13501/) (2023)
- [IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations](https://mlsanity.com/paper/2306.13865/) (2023)

## Abstract (arXiv)

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.