Predicting hypoglycemia in ICU patients: a machine learning approach
Bibliographic record
Abstract
BACKGROUND: The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan. RESEARCH DESIGN AND METHODS: The present study drew upon a substantial cohort of 13,567 patients admitted 26,248 times to the intensive care unit (ICU) over 10 years from July 2012 to July 2022. The primary outcome of interest was the occurrence of any hypoglycemic episode during the patient's ICU stay. Developing and testing predictor models was conducted using Python machine-learning libraries. RESULTS: A total of 1,896 were eligible to participate in the study, 206 experienced at least one hypoglycemic episode. Eight machine-learning models were trained to predict hypoglycemia. All models showed predicting power with a range of 74.53-99.69 for AUROC. Except for Naive Bayes, the six remaining models performed distinctly better than the basic logistic regression usually used for prediction in epidemiological studies. CatBoost model was consistently the best performer with the highest AUROC (0.99), accuracy and precision, sensitivity and specificity, and recall. CONCLUSIONS: We used machine learning to anticipate the likelihood of hypoglycemia, which can significantly decrease hypoglycemia incidents and enhance patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".