Using machine learning to discover correlations in MIMIC high-cadence data sets
Bibliographic record
Abstract
Abstract Using the MIMIC-IV clinical database and, specifically, the related preliminary MIMIC-IV waveform data release, we endeavored to create a machine learning tool in the form of a gated recurrent unit (GRU) neural network. Our goal was to study the possibility that intrusive measurements of vital signs, such as blood pressure, might be reliably predicted from parallel time series data sets of non-intrusive measurements of other vital signs, such as heart and respiratory rates. Relying on non-intrusive measurements to alert health care professionals of potentially life-threatening conditions in a timely manner can be life-saving in certain situations. To this end we developed and implemented a custom GRU solution in the form of software that can be run in a Web browser to analyze the high cadence numerical data sets of the MIMIC-IV waveform release. Despite the limitations of this data set, such as the small number of records, inconsistencies in what has been recorded, and often, noisy or incomplete data, we were able to obtain potentially promising results.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".