Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Purpose: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.

Methods: We reviewed EHRs of patients aged 1 month to 21 years from Boston Children's Hospital (BCH). We included all patients with convulsive ESE or RSE during admission. We employed and validated a pre-trained NLP tool, Document review Tool (DrT), to identify patients from 2013-2020, excluding training years (2017-2019). DrT notes a machine-learning score based on a support vector machine (SVM) and bag-of-n-grams. Higher scores indicated more likely ESE/RSE cases. To further evaluate the effectiveness of DrT-assisted review, we compared the results to human-reviewed notes from the pediatric Status Epilepticus Research Group (pSERG) consortium at BCH.

Results: The pre-trained algorithm identified 170 patients with RSE using DrT (Sensitivity: 98.8%), compared to 116 patients identified during human review (Sensitivity: 67.4%). Additionally, we identified 207 patients with ESE using DrT (Sensitivity: 99.5%), compared to 91 patients identified using human review (Sensitivity: 43.8%). Overall, DrT missed 3 cases (2 RSE and 1 ESE cases) that were identified during human review and identified 173 cases (56 RSE and 117 ESE cases) that were not found during the human review.

Conclusion: DrT-assisted manual review demonstrated higher sensitivity in identifying patients with ESE and RSE than the current standard of human review. This suggests that in contexts characterized by resource constraints NLP-related software like DrT can considerably enhance patient identification for research studies, treatment protocols, and preventative care interventions.