A Pilot Study Using Machine Learning for Classification of Pain-Related Versus Non-Pain-Related Electroencephalographic Activity in Preterm Infants
Bibliographic record
Abstract
Effective pain assessment and management are crucial to mitigate both immediate and long-term consequences of prolonged NICU stays. Accurately assessing pain in premature infants is challenging due to their inability to verbally communicate their pain, the potential judgement bias by caregivers, the lack of specificity in current pain assessment tools and time constraints in a busy hospital environment. This pilot study explores a machine learning approach to support pain assessment in neonatal care using cortical activity. The current study aims to test machine learning models that autonomously distinguishes non-pain related from pain-related cortical activity. The present dataset includes 72 preterm infants (27 females), born between 24- and 36-weeks gestational age, from two NICUs: Mount Sinai Hospital (Toronto, Canada) and University College London Hospital (London, UK). The primary outcome was to assess the accuracy of various machine learning models (XGBoost, Support Vector Machines, Random Forest, Logistic Regression, Convolutional Neural Networks) in distinguishing EEG features within a one-second pre-lance epoch (non-pain related) from a one-second post-lance epoch (pain-related). Performance metrics varied across post-menstrual age groups, reflecting developmental differences in EEG patterns. Machine learning algorithms can autonomously distinguish the one-second epoch immediately following a heel lance from the one-second epoch immediately preceding the procedure in preterm infants. Moreover, the performance of these algorithms improves with increasing postmenstrual age, demonstrating greater accuracy and reliability in older infants. This study provides a foundation for developing an autonomous and accurate tool for pain assessment in neonatal patients that can improve pain management practices in NICUs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".