Abstract 10386: Supervised Machine Learning for Relating Echocardiographic Parameters to Invasive Pressure Measurements in Pediatric Diastolic Function Assessment
Bibliographic record
Abstract
Introduction: Diagnosing diastolic dysfunction (DD) non-invasively in children is challenging as no validated pediatric diagnostic algorithm is available. The aim of this study is to use machine learning (ML) to identify a model that integrates echocardiographic measurements to predict invasive hemodynamic markers of DD in children. Methods: We enrolled children with Kawasaki disease, heart transplant, aortic stenosis, and coarctation of the aorta undergoing left heart catheterization. We obtained simultaneous invasive and echo DD measurements. We applied random forest (RF) algorithms to develop separate models for each cath marker (time constant of isovolumic relaxation (Tau), LVEDP, and -dP/dt max) and used demographics, diagnosis, and echo features as inputs. Model approximation was done using a regression tree with the top ranked features of each RF model to improve model interpretability (Figure 1). Spearman correlations were also assessed. Results: 59 children were included. Spearman correlations were low. However, the RF models' adjusted R 2 values in predicting Tau, LVEDP, and -dP/dt max are 0.62, 0.51, and 0.83, respectively. A representative ML-generated tree for LVEDP is shown in Figure 2. The most important features were propagation velocity (Vp) for Tau; E/Vp ratio for LVEDP; and systolic global longitudinal strain rate for -dP/dt max. Model approximation showed that a Vp < 42 cm/s predicted a Tau > 39 ms, and an E/Vp > 2.4 predicted an LVEDP > 13 mmHg. Conclusions: Predicting invasively measured diastolic parameters with echo data may be improved using ML algorithms. Model approximation may help better interpret the complex interactions in ML models.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".