Machine learning to predict outcomes of fetal cardiac disease: a pilot study
Bibliographic record
Abstract
Abstract BACKGROUND: Prediction of outcomes following a prenatal diagnosis of congenital heart disease is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. METHODS: We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict 1) in-utero or neonatal death, 2) high-acuity neonatal care and 3) favourable outcomes. We included all fetuses with cardiac disease at Sunnybrook Health Sciences Centre, Toronto, Canada, from 2012 – 2021. Prediction models were created using the XgBoost algorithm (tree-based) with 5-fold cross validation. RESULTS: Among 211 cases of fetal cardiac disease, 61 were excluded (39 terminations, 21 lost to follow-up, 1 isolated arrhythmia), leaving a cohort of 150 fetuses. Fifteen (10%) demised (10 neonates) and 70 (52%) of live births required high acuity neonatal care. Of those with clinical follow-up, 57/82 (70%) had a favourable outcome. Prediction models for live birth, high acuity neonatal care and favourable outcome had AUCs of 0.75, 0.82 and 0.72, respectively. The most important predictors for death were the presence of non-cardiac or genetic abnormalities and more severe structural heart disease. High acuity of postnatal care was predicted by increased nuchal thickness, lower gestational age (GA) and birthweight and favourable outcome was predicted by normal fetal right ventricular function, no tricuspid valve abnormalities, and normal GA/weight at birth. CONCLUSION Prediction models using ML provide good discrimination of key prenatal and postnatal outcomes among fetuses with congenital heart disease.
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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 | 0.008 | 0.014 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".