Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records.

ABSTRACT:

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 lea

SUBMITTER: Guo Y 

PROVIDER: S-EPMC11955907 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature