Precision Care Navigator: Predictive Analytics for Patient-Centered Healthcare (PCN-Patch)
Bibliographic record
Abstract
Developing analytics for hospitals' healthcare data to optimize patient care, streamline operations, and enhance overall efficiency is a crucial endeavor. However, existing systems often face challenges in accessing the right data in the correct format for analysis and struggle with accurately forecasting disease progression and patient readmission risks. To address these issues, we propose a hybrid machine learning algorithm designed to accurately predict disease progression and assess the risk of readmission. This innovative tool focuses on ST elevation myocardial infarction (STEMI), a cardiac condition requiring meticulous monitoring. The architecture predicts the progression level of STEMI using Logistic regression, yielding values from 0 to 10. The Risk Prediction component, employing a Neuro-Fuzzy algorithm, accurately categorizes risk into low, medium, high, and very high. This comprehensive approach aims to improve patient outcomes, optimize resource allocation, and enhance overall healthcare efficiency.
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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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".