Preventing catastrophes through PPG: feature extraction and critical event prediction from an ICU patient cohort
Bibliographic record
Abstract
Machine learning is steadily changing healthcare around the world, from smartwatches monitoring health behavior to NLP applications for automated diagnosis. Within this wide field of research, one promising subfield is represented by bedside applications in hospital’s intensive care units (ICUs). ICUs monitor closely the physiological state of a critical patient and some hospitals have started collecting data and researching for a way to assist the complex task of caring for a critical patient. One of these hospitals is the Sick Children Hospital of Toronto, which is collaborating to this thesis project. Together with Sick Children’s doctors we selected a cohort of ICU patients derived from MIT’s MIMIC-III database, singled out the PPG (photoplethysmogram) waveform signal and performed feature engineering in order to apply prediction models for critical events, such as circulatory failure. Other than trying to predict critical events the thesis aims to understand whether feature engineering in the medical context is still useful, given the automated feature extraction capabilities of modern ML techniques.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".