Health Guard: Patient Records and Wellness Detection

The “Health Guard: Wellness Detection and Patient Records” initiative involves developing a smart, data-driven solution for the better detection of diseases in proper interpretation of submitted symptoms by an individual. This uses complex machine learning algorithms in the analysis of a vast range of symptoms to match them with possible causes and suggest accurate, dependable predictions. In a patient records system, medical history is continuously tracked; therefore, it recognizes some form of pattern in recurring symptoms to provide health advice over time. The purpose of such a system is to deliver the best possible doctor in order to treat the maximum number of patients with proper medical care, be it a diagnostic purpose or an uninterrupted continuity, even if there is no doctor initially for diagnostics. It can indeed make a huge contribution by diagnosing patients early when an early medical check is appropriate. It is intended to provide timely and effective health care for the patient through a very interface that is intended to benefit both patients and healthcare providers when it is effectively integrated. This means that the system will always have security measures that safeguard sensitive health information, so personal information would never fall into wrong hands. The innovative system might remake the healthcare because it makes it more responsive to the needs of every patient and therefore leads to improved health outcomes across populations. Since the system evolves perpetually with the advancement of medical knowledge, the users can at best receive only the latest and most relevant health information available.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/icvadv63329.2025.10961097
Primary Topic
Traditional Chinese Medicine Studies
Type
article
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article

Health Guard: Patient Records and Wellness Detection

J. Jeya A Celin, Abhinaya Nakirikanti, Vadlamudi Pavani, Sravani Namburu et al.
Traditional Chinese Medicine Studies
article

Health Guard: Patient Records and Wellness Detection

J. Jeya A Celin, Abhinaya Nakirikanti, Vadlamudi Pavani, Sravani Namburu, Anamika
article en

Abstract

The “Health Guard: Wellness Detection and Patient Records” initiative involves developing a smart, data-driven solution for the better detection of diseases in proper interpretation of submitted symptoms by an individual. This uses complex machine learning algorithms in the analysis of a vast range of symptoms to match them with possible causes and suggest accurate, dependable predictions. In a patient records system, medical history is continuously tracked; therefore, it recognizes some form of pattern in recurring symptoms to provide health advice over time. The purpose of such a system is to deliver the best possible doctor in order to treat the maximum number of patients with proper medical care, be it a diagnostic purpose or an uninterrupted continuity, even if there is no doctor initially for diagnostics. It can indeed make a huge contribution by diagnosing patients early when an early medical check is appropriate. It is intended to provide timely and effective health care for the patient through a very interface that is intended to benefit both patients and healthcare providers when it is effectively integrated. This means that the system will always have security measures that safeguard sensitive health information, so personal information would never fall into wrong hands. The innovative system might remake the healthcare because it makes it more responsive to the needs of every patient and therefore leads to improved health outcomes across populations. Since the system evolves perpetually with the advancement of medical knowledge, the users can at best receive only the latest and most relevant health information available.

Kalasalingam Academy of Research and Education (IN)
Openalex Percentile: Top 88%
Traditional Chinese Medicine Studies
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Health Guard: Patient Records and Wellness Detection — J. Jeya A Celin, Abhinaya Nakirikanti, et al. · (2025) | TGRS Research Map | TGRS