Acute hypotension episode prediction using information divergence for feature selection, and non-parametric methods for classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it