Healthcare Monitoring AI Agent for Personalized Wellness Tracking and Intelligent Health Insights

This study introduces a tool made to help people manage meds, track body stats, set targets, pull in files (like CSV or JSON), plus search drug details - running through Streamlit with SQLite underneath. It ties together pill planning and workout logging along with visual feedback tools while letting users save reports as PDFs or ship out raw data. Tests showed folks found it easier to use, proving standalone features like reminder routines, target logs, and weekly summaries can boost consistency and self-monitoring even when no internet is around. We walk through how it’s built - the layout of tables, screen flow, ways it reads different inputs, math used to measure improvement - then share test reactions and what didn’t work well. Finally, we touch on hurdles that might pop up if rolled out widely, then sketch next steps such as linking real-time gadgets, shifting storage online, and adding smart warnings based on patterns.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-18
DOI
https://doi.org/10.5281/zenodo.17974819
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Healthcare Monitoring AI Agent for Personalized Wellness Tracking and Intelligent Health Insights

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare
article

Healthcare Monitoring AI Agent for Personalized Wellness Tracking and Intelligent Health Insights

article en

Abstract

This study introduces a tool made to help people manage meds, track body stats, set targets, pull in files (like CSV or JSON), plus search drug details - running through Streamlit with SQLite underneath. It ties together pill planning and workout logging along with visual feedback tools while letting users save reports as PDFs or ship out raw data. Tests showed folks found it easier to use, proving standalone features like reminder routines, target logs, and weekly summaries can boost consistency and self-monitoring even when no internet is around. We walk through how it’s built - the layout of tables, screen flow, ways it reads different inputs, math used to measure improvement - then share test reactions and what didn’t work well. Finally, we touch on hurdles that might pop up if rolled out widely, then sketch next steps such as linking real-time gadgets, shifting storage online, and adding smart warnings based on patterns.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 31%
Artificial Intelligence in Healthcare
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