Delirium prediction in the intensive care unit: a temporal approach
Bibliographic record
Abstract
The incidence of delirium in intensive care units is high and associated with poor outcomes; therefore, its prediction is desirable to establish preventive treatments. This retrospective study proposes a novel approach for delirium prediction. We analyzed static and temporal data from 10,475 patients admitted to one of 15 intensive care units (ICUs) in Alberta, Canada between January 1, 2014 and June 30, 2016. We tested 168 different combinations of study design parameters and five different predictive models (logistic regression, support vector machines, random forests, adaptive boosting and neural networks). The area under the receiver operating characteristic curve (AUROC) ranged from 0.754 (CI 95% ± 0.018) to 0.852 (± 0.033), with sensitivity and specificity respectively ranging from 0.739 (CI 95% ± 0.047) to 0.840 (CI 95% ± 0.064), and 0.770 (CI 95% ± 0.030) to 0.865 (CI 95% ± 0.038). These results are similar to previous studies; however, our approach allows for continuous updates and short-term prediction horizons which might provide major advantages.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".