Paper Type
Complete
Paper Number
PACIS2025-1340
Description
Mental health issues have been on the rise in recent years, mainly due to stress from various life experiences, which can lead to high blood pressure and heart-related illnesses. AI tools can analyse extensive datasets of stress-related experiences, with early detection being essential for managing interventions through the Internet of Health Things (IohT) and other AI applications. Using mapping and knowledge graph techniques, we have analysed photoplethysmography (PPG) measurements to interpret stress levels based on heart rate and body temperature. We have examined stress-related instances across several patients to assess their stress levels and recommend suitable referrals. We focus on developing systematic methodologies to integrate patients' wearable sensors and interpret their pulsations concerning stress-related conditions. We aim to develop and present a mental health knowledge graph framework to enhance the accuracy of stress level detection. A decision tree structure identifies stress levels at various decision nodes during pandemic periods.
Recommended Citation
Jha, Sambhavi; Nimmagadda, Shastri; Singh, Azad; Tripathi, Vishnu; and Mani, Neel, "AI and IoHT guided Bioinformatics Solutions – Managing Stress Related Mental Health" (2025). PACIS 2025 Proceedings. 12.
https://aisel.aisnet.org/pacis2025/ishealthcare/ishealthcare/12
AI and IoHT guided Bioinformatics Solutions – Managing Stress Related Mental Health
Mental health issues have been on the rise in recent years, mainly due to stress from various life experiences, which can lead to high blood pressure and heart-related illnesses. AI tools can analyse extensive datasets of stress-related experiences, with early detection being essential for managing interventions through the Internet of Health Things (IohT) and other AI applications. Using mapping and knowledge graph techniques, we have analysed photoplethysmography (PPG) measurements to interpret stress levels based on heart rate and body temperature. We have examined stress-related instances across several patients to assess their stress levels and recommend suitable referrals. We focus on developing systematic methodologies to integrate patients' wearable sensors and interpret their pulsations concerning stress-related conditions. We aim to develop and present a mental health knowledge graph framework to enhance the accuracy of stress level detection. A decision tree structure identifies stress levels at various decision nodes during pandemic periods.
Comments
Healthcare