Do epilepsy specialist nurses use a similar history-taking process as consultant neurologists in the differential diagnosis of patients presenting with a first seizure?

Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis

BACKGROUND

Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.

OBJECTIVE

We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.

METHODS

Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods.

RESULTS

Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985).

CONCLUSIONS

This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work.

The adolescent or adult with generalized tonic-clonic seizures

Primary and secondary generalized tonic-clonic seizures (GTCs) together constitute up to 50% of adolescent and adult patients with epilepsy as diagnosed by history and EEG. Syncope and psychogenic nonepileptic seizures are major differential diagnoses and must be carefully excluded in therapy-resistant cases. Individual episodes can have up to seven phases in secondarily generalized GTCs. The distinction between primary and secondary GTCs depends mainly on history and EEG, and yield can be improved with sleep deprivation or overnight recording. Epilepsies with primary or unclassified GTCs can respond to any one of the five broad-spectrum antiepileptic drugs (AEDs): valproate, lamotrigine, levetiracetam, topiramate and zonisamide. Unless a focal onset is clearly confirmed, a sodium-channel blocking AED should not be used in the initial treatment of these conditions.