Rehabilitation and Home Health Monitoring Based-AI Scheduling Application for Coronary Artery Disease and Cardiovascular Patients
Bibliographic record
Abstract
Cardiovascular and coronary artery disease diseases complications can be very serious, and it is crucial for a close monitoring and routine rehabilitation activities in order to help patients to get back on their normal lifestyle.Even though most individuals with COVID-19 did recover within weeks of ailment, a few individuals still encounter severe long COVID conditions and 'Silent Hypoxia'.Individuals commonly encounter distinctive combinations of long COVID symptoms such as difficulty in breathing, critical heart palpitations, worst sleep quality, dizziness on standing, etc. Silent hypoxia is a condition where patients have an extremely low oxygen level but do not show any symptoms of breathlessness.In order to navigate and resolve the above issues, this work proposes and implements a real-time monitoring towards the changes of patient health data using continuous clinical surveillance solution.The application is an appropriate rehabilitation course that plays a vital role for post discharge patients in providing an improvement for respiratory, cardiovascular, and psychological components.It is very important to provide the visibility of health data in terms of heart rate and VO2 max values to enable emergency respiratory support, important alerts and real-time monitoring.In this work, a novel configuration for home health monitoring and rehabilitation based-AI scheduling system is proposed for coronary artery disease and cardiovascular patients.The Android application introduces a secure health data sharing and smart alerting system to provide full surveillance towards the patient.It will enable the interaction between the smart wearable by using the health kit and artificial intelligence algorithms to schedule the best fit rehabilitation activity based on the patient's health status and live monitoring by medical practitioners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".