Summary of Diagnosis Extraction From Unstructured Dutch Echocardiogram Reports Using Span- and Document-level Characteristic Classification, by Bauke Arends et al.
Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification
by Bauke Arends, Melle Vessies, Dirk van Osch, Arco Teske, Pim van der Harst, René van Es, Bram van Es
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the feasibility of automatically extracting clinically accurate labels from unstructured Dutch echocardiogram reports. The researchers used 115,692 reports from the University Medical Center Utrecht and manually annotated a subset of 11 cardiac characteristics. They developed several automatic labeling techniques at both span and document levels, evaluating performance using F1-score, precision, and recall. The SpanCategorizer and MedRoBERTa.nl models outperformed others, with weighted F1-scores ranging from 0.60 to 0.93. Direct document classification was superior to indirect methods using span classifiers. SetFit achieved competitive results using only 10% of training data, making it a promising alternative for limited datasets. The study recommends using the published SpanCategorizer and MedRoBERTa.nl models for diagnosis extraction from Dutch echocardiography reports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to automatically identify important medical information in unorganized hospital records. They used over 115,000 records and had a team of experts label some parts of the reports. The researchers then created computer programs to do this job automatically. These programs were tested and compared, with some performing better than others. One program called MedRoBERTa.nl was particularly good at identifying certain types of medical information. Another program, SetFit, did well even when given only a small amount of training data. This study helps doctors and computers work together to get accurate medical information from these records. |
Keywords
» Artificial intelligence » Classification » F1 score » Precision » Recall
