A Generalized Machine Learning Model for Identifying Congenital Heart Defects (CHDs) Using ICD Codes.

ABSTRACT:

Background

International 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.

Methods

Traditional 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 classifi

SUBMITTER: Shi H 

PROVIDER: S-EPMC12027675 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature