An IoT Enabled Computational Model and Application Development for Monitoring Cardiovascular Risks
Bibliographic record
Abstract
The Internet of Things (IoT) has opened up a wide array of possibilities for healthcare and medical practitioners alike. This technology offers an effective platform to monitor and manage various aspects of a person's health, including cardiovascular risks. In this context, a computational model and application development play a significant role in monitoring risks, optimizing treatments, and creating personalized care plans. The model explores various parameters such as lifestyle, diet, and medications to predict the potential risks that may endanger a person's life. Artificial Intelligence algorithms are used to optimize the performance of the model. The techniques used for data collection, analysis, and interpretation have been adapted from data science, which allows for more accurate predictions. The data collected from the model is used to develop an application that can be used to monitor the health of the patient and provide appropriate advice on medication, diet, and lifestyle. This application can be used by both the medical practitioner and the patient, providing them with useful information on an ongoing basis. By combining the skills of design, engineering, and data science, the model and application can be used to improve outcomes and reduce the costs associated with cardiovascular treatment. It can also help to reduce the risks associated with heart disease and stroke, and create a better quality of life for patients suffering from various heart-related conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".