Towards the prediction of mortality in Intensive Care Units patients: A Simple Correspondence Analysis approach
Bibliographic record
Abstract
In the setting of the PhysioNet/CinC Challenge 2012 Event 1, a new method to predict in hospital mortality in the Intensive Care Units (ICU) is proposed. The predictor, retrieved by Simple Correspondence Analysis (SCA), is based on a combination of clinical and laboratory data with more traditional score systems such as APACHE-II and SAPS-II. Information from records out of 12000 ICU patients was equally divided in three sets: A, B and C. Up to 37 variables were recorded during the first 48h after admission to the ICU. Using Set A, SCA was applied to select the variables most related to patients mortality from their hospitalizations. The proposed predictor combines these variables using the traditional APACHE II and SAPS II scores.SCA results show that variables such as creatinine, urine output, bilirubin and mechanical ventilation support were capable to discriminate between patients who survive or do not survive their ICU stays. Using these variables, the prediction method provides a SCORE1=43.50 % using set A, SCORE1=42.25 % using set B and SCORE1=42.73% using set C, where SCORE1 is defined as min(sensibility, positive predictivity). These results represent an improvement of 14 % in SCORE1 when compared with traditional score SAPS-I (43.50 % vs. 29.60%).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".