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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.
Insights
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.
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.
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.
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