Abstract 14955: Machine Learning Predicts Hemodynamic Instability in Children After Cardiac Surgery in Pediatric Intensive Care Unit (PICU)
Bibliographic record
Abstract
Introduction: Despite improvements in management for children after cardiac surgery, a non-negligible proportion of patients suffer from cardiac arrest, having a poor prognosis. Although serum lactate levels are widely accepted markers of hemodynamic instability, measuring lactate requires discrete blood sampling. An alternative method to evaluate hemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. Hypothesis: We hypothesize that blood lactate in PICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight children, who underwent heart surgery, were included. Patient characteristics and physiological measurements were acquired and analyzed using specialized software/hardware, including heart rate, lactate level, arterial waveform sharpness, and area under the curve. Predicting a patient’s blood lactate levels was accomplished using regression-based supervised learning algorithms, including regression decision trees, tuned decision trees, random forest regressor, tuned random forest, AdaBoost regressor, and hypertuned AdaBoost. All algorithms were compared with hold-out cross validation. Two approaches were considered: basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. The second approach supports updating the learning system’s predictive capacity whenever a patient has a new ground truth blood lactate reading acquired. Results: In both approaches, the best performing machine learning method was the tuned random forest, which yielded a mean absolute error of 5.60 mg/dL in the first approach, and 4.62 mg/dL when predicting blood lactate with updated ground truth. Conclusions: In conclusion, the tuned random forest is capable of predicting the level of serum lactate by analyzing perioperative variables, including the arterial pressure waveform. Machine learning can predict the patient’s hemodynamics non-invasively, continuously, and with accuracy that may demonstrate clinical utility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".