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

Molly Ann Puckett1, Fatemeh Mohammad Alizadeh Chafjiri1, Jennifer V Gettings1

  • 1Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA.

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Summary

Natural language processing (NLP) tools like Document review Tool (DrT) significantly improve the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) compared to manual review.

Keywords:
Electronic Health RecordNatural language processingPediatricSeizureStatus epilepticus

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Area of Science:

  • Medical Informatics
  • Neurology
  • Artificial Intelligence in Healthcare

Background:

  • Electronic health records (EHR) are crucial for clinical research and patient care.
  • Identifying patients with specific neurological conditions like established status epilepticus (ESE) and refractory status epilepticus (RSE) from EHRs can be challenging and time-consuming.
  • Current methods often rely on manual chart review, which may be limited by resource constraints and potential for human error.

Purpose of the Study:

  • To compare the effectiveness of natural language processing (NLP) assisted review versus traditional human review for identifying patients with ESE and RSE in EHRs.
  • To evaluate the sensitivity and accuracy of a pre-trained NLP tool (Document review Tool - DrT) in detecting these specific patient cohorts.
  • To assess the potential of NLP tools to enhance patient identification for research and clinical applications.

Main Methods:

  • Utilized EHR data from pediatric patients (1 month to 21 years) at Boston Children's Hospital (BCH).
  • Employed a pre-trained NLP tool (DrT) utilizing machine learning (SVM and bag-of-n-grams) to identify patients with convulsive ESE or RSE.
  • Compared DrT-identified cases against a gold standard of human-reviewed notes from the pediatric Status Epilepticus Research Group (pSERG) consortium.

Main Results:

  • DrT demonstrated significantly higher sensitivity in identifying patients with RSE (98.8%) and ESE (99.5%) compared to human review (67.4% for RSE, 43.8% for ESE).
  • DrT identified substantially more patients with RSE (170 vs. 116) and ESE (207 vs. 91) than human review.
  • While DrT missed 3 cases, it identified 173 additional cases not found through manual review, highlighting its comprehensive detection capabilities.

Conclusions:

  • NLP-assisted review using DrT significantly outperforms standard human review in identifying patients with ESE and RSE.
  • DrT offers a more sensitive and efficient method for patient cohort identification from EHR data.
  • NLP tools like DrT can be invaluable in resource-limited settings to improve patient identification for research, treatment protocols, and preventative care.