Predicting family physicians based on their practice using machine learning
Why this work is in the frame
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Bibliographic record
Abstract
Significant research has been done in the medical domain using machine learning and clinical data sets. Although there are many interesting and influential clinical research works in the fields of healthcare and health services using machine learning, there is a need to apply machine learning in the field of health human resource planning. This study uses physician billing data and machine learning to identify and classify family physicians with the goal of improving health human resource planning. This research is essential for policy makers because it is important to know the number of family physicians practicing in certain geographical regions for providing timely care. Additionally, this issue becomes particularly important when it comes to serving communities with fewer resources such as the rural areas of Northwestern Ontario, where family physicians need to work to their full scope of practice, provide more services than physicians working in urban areas, to meet the needs of patients. In this study, recursive feature elimination method is used to reduce the number of predictors for the classification problems. As the result of this process, the most important features include physician’s rurality, full-time equivalent hours, age, and years of experience. Further, several machine learning models are used to solve binary and multi-class classification problems. Gradient boosting machine learning was the most accurate in predicting family physician practice, with a receiver operating characteristic value, ROC value, of 0.73 and 0.72 for binary and multi-class classification, respectively.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it