Acute hypotension episode prediction using information divergence for feature selection, and non-parametric methods for classification
Bibliographic record
Abstract
Acute hypotension is a critical event that can lead to irreversible organ damage and death. When detected in time, an appropriate intervention can significantly lower the risks for the patient. The objective of this work is to describe an automated statistical method that produces an automated method to predict acute hypotension episodes, using the least data possible. We first detailed the problem of having more features than samples in the PhysioNet/CinC Challenge 2009 training set. We constrained our analysis to the largest common subset of features available for all patients (arterial blood pressure measurements). We then used information divergence (or Kullback-Liebler divergence) between two distributions to identify the most discriminative features. We used these features in each training set to classify the samples in the test sets using a nearest neighbors (NN) algorithm. With this method, we obtained a score of 9/10 for event 1, and 32/40 for event 2 compared to a control method which gives us 10/10 for event 1, and 35/40 for event 2. Our preliminary results showed that our method leads to significantly better than random results, therefore it increases our information about the samples in the test sets.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".