Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence Framework
Bibliographic record
Abstract
Abstract Background & Motivation: The field of personalized medicine endeavors to transform the healthcare industry by advancing individualized strategies for diagnosis, treatment modalities, and prognostic assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Medical practitioners can use this strategy to tailor early interventions for each patient's explicit treatment or preventative requirements. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: In this comprehensive analysis, we conducted a detailed examination of the term "personalized medicine," delving into its fundamental principles, the obstacles it encounters as an emerging subject, and its potentially revolutionary implications in the domain of CVD. A total of 228 studies were selected using the PRISMA methodology. Findings and Conclusions : Herein, we provide a scoping review highlighting the role of AI, particularly DL, in personalized risk assessment for CVDs. It underscores the prospect for AI-driven personalized medicine to significantly improve the accuracy and efficiency of controlling CVD, revolutionizing patient outcomes. The article also presents examples from real-world case studies and outlines potential areas for future research.
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How this classification was reachedexpand
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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".