Purpose: Visual acuity (va) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. Th...
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Purpose: Visual acuity (va) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of va measurements and their lateralities from free-text ophthalmology notes: va for each of the right and left eyes, with and without glasses correction, and with and without pinhole. Design: Cross-sectional study. Subjects: A total of 319 756 clinical notes with documented va measurements from approximately 90 000 patients were included. Methods: The notes were split into train, validation, and test sets. Bidirectional Encoder Representations from Transformers (BERT) models were fine-tuned to identify va measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based al-gorithm was created to recognize the same va entities to compare against BERT models. Main Outcome Measures: Model performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities. Results: On the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling va in sections outside of the examination portion of the note, difficulties labeling current va alongside a series of past vas, and missing nonnumeric vas. Conclusions: This study demonstrates that deep learning models are capable of identifying va measurements from free-text ophthalmology notes with high precision and recall, achieving significant perfor
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