Artificial Intelligence-Based Cardiac Rehabilitation Therapy Exercise Recommendation System
Bibliographic record
Abstract
Cardiovascular disease is one of the leading causes of death in modern society. There are many factors that must be taken into consideration when analyzing cardiovascular disease (CVD), such as a patient's quality of life, general well-being, diet, lifestyle, psychosocial effects, medical costs, and many other factors. Pharmaceutical interventions are the primary method of treating heart conditions, however, one of the most popular and widespread methods of treatment which often augments medication is Cardiac Rehabilitation Therapy (CRT). CRT programs have been shown to be an effective secondary prevention method for CVD. It can help reduce patient risk, as well as improve their recovery and overall health outcomes. In this paper, we propose a recommendation system for patient decision support, and a computational model for cardiac rehabilitation planning, in order to personalize and optimize the physical exercise therapy plan offered to patients, to meet their goals and needs. We tested our proposed model against randomly generated exercise plans, and found that our model was able to find the optimal therapy plan with a 43% higher rate of success on average relative to the random model, with regards to meeting patients' goals and needs. We believe our patient-centered solution to CRT will improve patient satisfaction, quality of life, and help reduce costs through improved efficacy and adherence to the therapy program, along with improved post-therapy cardiac health management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".