ArticleBirth defects research2025
A Generalized Machine Learning Model for Identifying Congenital Heart Defects (CHDs) Using ICD Codes.
Article in Birth defects research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
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13 authors.
Funding
Abstract
backgroundInternational Classification of Diseases (ICD) codes utilized for congenital heart defect (CHD) case identification in datasets have substantial false-positive (FP) rates. Incorporating machine learning (ML) algorithms following case selection by ICD codes may improve the accuracy of CHD identification, enhancing surveillance efforts.
methodsTraditional ML methods were applied to four encounter-level datasets, 2010-2019, for 3334 patients with validated diagnoses and with at least one CHD ICD code identified. A 5-fold cross-validation approach was applied to the dataset to determine the set of overlapping important features best classifying CHD cases. Training and testing combinations were explored to determine the approach yielding the most accurate CHD classification.
resultsCHD ICD positive predictive values (PPVs) by site ranged from 53.2% to 84.0%. The ML algorithm achieved a PPV of 95% (1273/1340) for the four-site dataset with a false-negative (FN) rate of 33% (639/1912) by choosing an operating point prioritizing PPV from the PPV-FN rate curve. XGBoost reduced 2105 Clinical Classification Software (CCS) features to 137 that identified those with true-positive (TP) CHD and false-positive FP classification.
conclusionApplying ML algorithms following case selection by CHD-related ICD codes improved the accuracy of identifying TP true-positive CHD cases.
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