Faculty and Staff Publications
Publication Date
1-1-2023
Journal
AMIA Joint Summits on Translational Science Proceedings
Abstract
Early onset of seizure is a potential risk factor for Sudden Unexpected Death in Epilepsy (SUDEP). However, the first seizure onset information is often documented as clinical narratives in epilepsy monitoring unit (EMU) discharge summaries. Manually extracting first seizure onset time from discharge summaries is time consuming and labor-intensive. In this work, we developed a rule-based natural language processing pipeline for automatically extracting the temporal information of patients’ first seizure onset from EMU discharge summaries. We use the Epilepsy and Seizure Ontology (EpSO) as the core knowledge resource and construct 4 extraction rules based on 300 randomly selected EMU discharge summaries. To evaluate the effectiveness of the extraction pipeline, we apply the constructed rules on another 200 unseen discharge summaries and compare the results against the manual evaluation of a domain expert. Overall, our extraction pipeline achieved a precision of 0.75, recall of 0.651, and F1-score of 0.697. This is an encouraging initial result which will allow us to gain insights into potentially better-performing approaches.