# IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being

arXiv: https://arxiv.org/abs/2406.13791 · HTML: https://mlsanity.com/paper/2406.13791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 94 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?](https://mlsanity.com/paper/2010.08660/) (2020)
- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework](https://mlsanity.com/paper/2403.00781/) (2024)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety](https://mlsanity.com/paper/2312.06798/) (2023)
- [Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step](https://mlsanity.com/paper/1510.04221/) (2015)
- [Knowledge Graphs and Knowledge Networks: The Story in Brief](https://mlsanity.com/paper/2003.03623/) (2020)
- [Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations](https://mlsanity.com/paper/2405.02228/) (2024)
- [WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions](https://mlsanity.com/paper/2406.12058/) (2024)
- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
- [Process Knowledge-infused Learning for Clinician-friendly Explanations](https://mlsanity.com/paper/2306.09824/) (2023)
- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)
- ["What's ur type?" Contextualized Classification of User Types in Marijuana-related Communications using Compositional Multiview Embedding](https://mlsanity.com/paper/1806.06813/) (2018)
- [Comparing Suicide Risk Insights derived from Clinical and Social Media data](https://mlsanity.com/paper/2012.11393/) (2020)

## Abstract (arXiv)

Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twins (DTs) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). DTs facilitate monitoring and provide constant health insight to improve quality of life and well-being with better personalization. Healthcare DTs challenges are standardizing data formats, communication protocols, and data exchange mechanisms. As an example, ISO has the ISO/IEC JTC 1/SC 41 Internet of Things (IoT) and DTs Working Group, with standards such as "ISO/IEC 21823-3:2021 IoT - Interoperability for IoT Systems - Part 3 Semantic interoperability", "ISO/IEC CD 30178 - IoT - Data format, value and coding". To achieve those data integration and knowledge challenges, we designed the Mental Health Knowledge Graph (ontology and dataset) to boost mental health. As an example, explicit knowledge is described such as chocolate contains magnesium which is recommended for depression. The Knowledge Graph (KG) acquires knowledge from ontology-based mental health projects classified within the LOV4IoT ontology catalog (Emotion, Depression, and Mental Health). Furthermore, the KG is mapped to standards when possible. Standards from ETSI SmartM2M can be used such as SAREF4EHAW to represent medical devices and sensors, but also ITU/WHO, ISO, W3C, NIST, and IEEE standards relevant to mental health can be considered.