M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we present M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease, a system designed specifically for mobile devices that facilitates monitoring of cardiovascular disease (CVD). The system uses wearable sensors to collect observable trends of vital signs contextualized with data from clinical databases. Instead of transferring the raw data directly to the health care professionals, the system performs analysis on the local device by feeding the hybrid of collected data to a support vector machine (SVM) to monitor features extracted from clinical databases and wearable sensors to classify a patient as “continued risk” or “no longer at risk” for CVD. As a work in progress we evaluate a proof-of-concept M4CVD using a synthetic clinical database of 200 patients. The results of our experiment show the system was successful in classifying a patient's CVD risk with an accuracy of 90.5%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it