Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification

TL;DR

This study demonstrates effective automatic extraction of cardiac diagnosis labels from unstructured Dutch echocardiogram reports using span- and document-level classification models, improving efficiency and accuracy in clinical labeling.

Contribution

Introduces and evaluates novel span- and document-level classification models for extracting diagnoses from Dutch echocardiogram reports, with models outperforming existing methods.

Findings

SpanCategorizer achieved F1-scores up to 0.93 for span classification.

MedRoBERTa.nl achieved F1-scores up to 0.98 for document classification.

SetFit performs well with limited training data, near-perfect results with fewer labels.

Abstract

Clinical machine learning research and AI driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists is often time-consuming and expensive. This study tests the feasibility of automatic span- and document-level diagnosis extraction from unstructured Dutch echocardiogram reports. We included 115,692 unstructured echocardiogram reports from the UMCU a large university hospital in the Netherlands. A randomly selected subset was manually annotated for the occurrence and severity of eleven commonly described cardiac characteristics. We developed and tested several automatic labelling techniques at both span and document levels, using weighted and macro F1-score, precision, and recall for performance evaluation. We compared the performance of span labelling against document labelling methods, which included both direct document classifiers and indirect document classifiers that rely on span classification results. The SpanCategorizer and MedRoBERTa$.$nl models outperformed all other span and document classifiers, respectively. The weighted F1-score varied between characteristics, ranging from 0.60 to 0.93 in SpanCategorizer and 0.96 to 0.98 in MedRoBERTa$.$nl. Direct document classification was superior to indirect document classification using span classifiers. SetFit achieved competitive document classification performance using only 10% of the training data. Utilizing a reduced label set yielded near-perfect document classification results. We recommend using our published SpanCategorizer and MedRoBERTa$.$nl models for span- and document-level diagnosis extraction from Dutch echocardiography reports. For settings with limited training data, SetFit may be a promising alternative for document classification.