MiPy: A Framework for Benchmarking Machine Learning Prediction of Unplanned Hospital and ICU Readmission in the MIMIC-IV Database
Bibliographic record
Abstract
Abstract Avoidable and unplanned readmissions to hospital wards, especially the Intensive Care Unit, have significant implications for the patients’ health and poses additional economic burdens on the health system. If patients who are at risk of readmission are identified early and their risks are mitigated, these complications can be avoided. Machine Learning has been a valuable tool for automatic identification and prediction of various health conditions and situations, including unplanned readmissions. This is made possible through processing large collections of clinical data to build predictive models. However, the clinical data from which these models are built is highly confidential, which has restricted the ability of researchers to provide their data to the wider community, hence limiting reproducibility and comparability between results. The MIMIC databases are large, publicly available clinical datasets, which make reproducibility and comparability feasible. To maximise the benefit the research community derives from this invaluable resource, we developed MiPy, an open source standardised framework for preparing, building, and evaluating machine learning models for predicting both hospital and ICU readmission, on the MIMIC-IV database. The primary aim of this work is to enhance reproducibility and comparability of research in the field.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".