A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) describes a long-term decline in kidney function and has many causes. It affects hundreds of millions of people worldwide every year. It can have a strong negative impact on patients, especially when combined with cardiovascular disease (CVD): patients with both conditions have lower survival chances. In this context, computational intelligence applied to electronic health records can provide insights to physicians that can help them make better decisions about prognoses or therapies. In this study we applied machine learning to medical records of patients with CKD and CVD. First, we predicted if patients develop severe CKD, both including and excluding information about the year it occurred or date of the last visit. Our methods achieved top mean Matthews correlation coefficient (MCC) of +0.499 in the former case and a mean MCC of +0.469 in the latter case. Then, we performed a feature ranking analysis to understand which clinical factors are most important: age, eGFR, and creatinine when the temporal component is absent; hypertension, smoking, and diabetes when the year is present. We then compared our results with the current scientific literature, and discussed the different results obtained when the time feature is excluded or included. Our results show that our computational intelligence approach can provide insights about diagnosis and and relative important of different clinical variables that otherwise would be impossible to observe.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".