Predicting exercise pulmonary hypertension: the right-net machine learning model a pilot study
Bibliographic record
Abstract
BACKGROUND: Exercise-transthoracic Doppler echocardiography (Ex-TTE) determination of mean pulmonary arterial pressure (mPAP)/cardiac output (CO) slope may offer key diagnostic and prognostic information in cardiorespiratory diseases. However, its applicability and reliability in routine clinical practice remain to be established. Herein, the aim of the present study was to apply a machine learning (ML) model to predict abnormal exercise TTE-derived mPAP/CO slope (>3 mmHg/L·min) in individuals at risk of pulmonary hypertension (PH), based only on clinical and resting TTE parameters. METHODS: The study population (221 healthy adults and 196 patients with connective tissue disease) was grouped according to mPAP/CO slope ≤3 vs. >3 mmHg/L·min (n = 222 and n = 195, respectively). Three different ML models (Elastic Net-Regularized Generalized Linear Model, Classification and Regression Tree, LogitBoost) were trained on resting clinical and TTE parameters to predict mPAP/CO slope >3 mmHg/L·min. Data were split into training/test sets to evaluate performance. The model with the highest area under the curve (AUC) on the test set was selected. RESULTS: The Elastic Net model achieved the best performance (AUC=0.92). Lower tricuspid annular plane systolic excursion/systolic PAP ratio, female sex, and smaller left ventricular outflow tract diameter were the key features predicting TTE-derived mPAP/CO slope >3 mmHg/L·min. CONCLUSIONS: An ML algorithm using resting clinical and TTE parameters can effectively predict exercise TTE-derived mPAP/CO slope >3 mmHg/L·min, supporting its use as a noninvasive tool to identify individuals at risk of exercise PH.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".