Natural language processing for identification of refractory status epilepticus in children.

Journal: Epilepsia
PMID: 37804085

Abstract

OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic health records (EHRs) permits analysis of care approaches and disease outcomes at a lower cost than prospective research. However, reviewing EHR manually is time intensive. We aimed to compare refractory status epilepticus (rSE) cases identified by human EHR review with a natural language processing (NLP)-assisted rSE screen followed by a manual review.

Authors

  • Fatemeh Mohammad Alizadeh Chafjiri
    Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Latania Reece
    Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Lillian Voke
    Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Assaf Landschaft
  • Justice Clark
    Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Amir A Kimia
    From the *Department of Medicine, Division of Emergency Medicine, Boston Children's Hospital; †Children's Informatics Program; and ‡Boston Children's Hospital IT, and Department of Infectious Diseases, Boston, MA.
  • Tobias Loddenkemper
    Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA.

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