Key result
A machine learning and natural language processing approach using clinical notes enabled more fine-grained classification of atrial septal defects compared to ICD codes (score 0.39 ±0.03).
Why the study?
The ICD code for secundum ASD has a low positive predictive value for CHD, necessitating methods with reduced false positive rates for public health surveillance.
Does a machine learning and NLP-based classification system improve the positive predictive value for congenital heart defects among patients with an ASD ICD code compared to ICD codes alone?
Does a machine learning and NLP-based classification system improve the positive predictive value for congenital heart defects among patients with an ASD ICD code compared to ICD codes alone?
Using clinical notes and machine learning enables more fine-grained classification of atrial septal defects with a higher positive predictive value for congenital heart disease than ICD codes alone.
BACKGROUND: International Classification of Disease (ICD) codes can accurately identify patients with certain congenital heart defects (CHDs). In ICD-defined CHD data sets, the code for secundum atrial septal defect (ASD) is the most common, but it has a low positive predictive value for CHD, potentially resulting in the drawing of erroneous conclusions from such data sets. Methods with reduced false positive rates for CHD among individuals captured with the ASD ICD code are needed for public health surveillance. METHODS: We propose a two-level classification system, which includes a CHD and an ASD classification model, to categorize cases with an ASD ICD code into three groups: ASD, other CHD, or no CHD (including patent foramen ovale). In the proposed approach, a machine learning model that leverages structured data is combined with a text classification system. We compare performances for three text classification strategies: support vector machines (SVMs) using text-based features, a robustly optimized Transformer-based model (RoBERTa), and a scalable tree boosting system using non-text-based features (XGBoost). RESULTS: score: 0.39 [±0.03]). CONCLUSIONS: This study demonstrates that it is feasible to use patients' clinical notes and machine learning to perform more fine-grained classification compared to ICD codes, particularly with higher PPV for CHD. The proposed approach can improve CHD surveillance.
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Guo et al. (2025) studied Atrial Septal Defects (ASD) and Congenital Heart Defects (CHD). Machine learning and natural language processing classification system vs. ICD codes alone was evaluated on Classification performance (score). A machine learning and natural language processing approach using clinical notes enabled more fine-grained classification of atrial septal defects compared to ICD codes (score 0.39 ±0.03).
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