Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing.

ABSTRACT:

Objective

Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If successful, this could improve clinical decision-making in epilepsy patients and allow for rapid, large-scale retrospective research.

Materials and methods

We developed a finetuning pipeline for pretrained neural models to classify patients as being seizure-free and to extract text containing their seizure frequency and date of last seizure from clinical notes. We annotated 1000 notes for use as training and testing data and determined how well 3 pretrained neural models, BERT, RoBERTa, and Bio_ClinicalBERT, could identify and extract th

SUBMITTER: Xie K 

PROVIDER: S-EPMC9006692 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature