AI-Powered Personalized Healthcare Monitoring

Nidhi Haridas; Ananya Bhat; Safrina Bellary; Lavanya Salian; Madhvi Saxena; Prashant Saxena

doi:10.2991/978-94-6239-727-9_10

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AI-Powered Personalized Healthcare Monitoring

Authors

Nidhi Haridas¹, Ananya Bhat¹, Safrina Bellary¹, Lavanya Salian¹, Madhvi Saxena¹^{, *}, Prashant Saxena²

¹Dept. of Artificial Intelligence Vishwakarma University, Pune, India

²Dept. of Bioinformatics Chandigarh University, Chandigarh, India

^*Corresponding author. Email: madhvi.saxena@vupune.ac.in

Corresponding Author

Madhvi Saxena

Available Online 22 July 2026.

DOI: 10.2991/978-94-6239-727-9_10 How to use a DOI?
Keywords: Personalized monitoring; deep learning; data structures; anomaly detection; edge computing; healthcare informatics; privacy
Abstract: Many of today’s monitoring technologies are based on scheduled check-ins which can introduce delays in dealing with chronic disease. To overcome this disadvantage, in this work, we propose an artificial intelligence based Personalized Healthcare Monitoring System that employs Deep Learning and optimized Data Structures for continuous and predictive monitoring of patients. This proposal is a new system that uses embedded data analytics to achieve early warnings of anomalies so that interventions can be imposed before deleterious effects occurs. We are suggesting CNNs (Continuous Neural Networks), LSTMs (Long Short-Term Memory Networks), and Autoencoders as predictive tools for risk as we work with linked lists, hash maps, trees, and queues to help us alert users to alert users to/and manage this data. With this being said, the system is scalable, allowing proactive, real-time care of patients, where ever they are, in hospitals, in the home and with telehealth.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Sustainable Micro-Nano Materials & Innovative Technology (ICSUMMIT 2026)
Series: Atlantis Highlights in Engineering
Publication Date: 22 July 2026
ISBN: 978-94-6239-727-9
ISSN: 2589-4943
DOI: 10.2991/978-94-6239-727-9_10 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Nidhi Haridas
AU  - Ananya Bhat
AU  - Safrina Bellary
AU  - Lavanya Salian
AU  - Madhvi Saxena
AU  - Prashant Saxena
PY  - 2026
DA  - 2026/07/22
TI  - AI-Powered Personalized Healthcare Monitoring
BT  - Proceedings of the International Conference on Sustainable Micro-Nano Materials & Innovative Technology (ICSUMMIT 2026)
PB  - Atlantis Press
SP  - 126
EP  - 137
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-727-9_10
DO  - 10.2991/978-94-6239-727-9_10
ID  - Haridas2026
ER  -

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