Interpretable model committee for monitoring and early prediction of intracranial pressure crises
Bibliographic record
Abstract
Traumatic brain injury (TBI) often leads to elevated intracranial pressure (ICP), a dangerous complication associated with increased morbidity and mortality. This study introduces a novel artificial intelligence-based solution for predicting potentially life threatening ICP crises by considering high ICP burden (pressure–time dose), impaired cerebrovascular reactivity, and reduced cerebrospinal compensatory reserve. We propose a committee of models that uses explainable TabNet structures to predict ICP crisis events and identify the most important features driving the predictions to provide additional insights into ICP crisis development over time. The model’s performance was assessed in a large multi-center dataset of long-term high-resolution recordings of physiological signals (ICP and arterial blood pressure) collected in 749 TBI patients. The model achieved very high recall (0.94) with lower precision (0.34) in predicting rare critical events 30 min in advance based on 4 h of previous signal-derived metrics. Explainability analysis revealed that the predictions are primarily influenced by dynamic changes in cerebrospinal pressure–volume relationships which supports the view that patients with reduced cerebrospinal pressure buffering capacity may be at higher risk of presenting ICP elevation. In addition to a well-performing model for ICP crisis prediction in TBI validated in a large dataset, the proposed solution offers the potential for real-time monitoring of feature importance which could help inform the clinical decision-making process and enhance patient outcomes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".