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Integrating structured and unstructured data for predicting emergency severity: an association and predictive study

Xingyu Zhang1, Yanshan Wang2, Yun Jiang3

  • 1Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA. xiz261@pitt.edu.

BMC Medical Informatics and Decision Making
|December 5, 2024
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Summary

Combining structured and unstructured patient data significantly improves emergency department (ED) triage accuracy. Utilizing clinical notes with machine learning enhances severity prediction and patient outcomes.

Keywords:
Association studyClinical decision supportEmergency departmentNatural language processingPredictive modelingUnstructured data

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Emergency department (ED) triage is crucial for timely patient care.
  • Traditional triage relies on structured data, but unstructured clinical notes offer potential for enhanced predictive modeling.
  • This study evaluated combining structured and unstructured data for improved emergency severity prediction.

Purpose of the Study:

  • To assess the effectiveness of integrating structured and unstructured data for predicting emergency severity.
  • To explore associations between patient characteristics and emergency severity outcomes.
  • To compare the performance of machine learning models using different data configurations.

Main Methods:

  • Utilized 2021 National Hospital Ambulatory Medical Care Survey (NHAMCS) data for adult ED patients.
  • Categorized emergency severity using the Emergency Severity Index (urgent: 1-3, non-urgent: 4-5).
  • Processed unstructured data (chief complaints, reasons for visit) with a Bidirectional Encoder Representations from Transformers (BERT) model; applied Logistic Regression, Random Forest, Gradient Boosting, and Extreme Gradient Boosting to structured, unstructured, and combined data.

Main Results:

  • The study included 8,716 adult patients; 74.6% were classified as urgent.
  • Significant predictors of severity included older age, elevated heart rate, chronic kidney disease, and coronary artery disease.
  • Gradient Boosting with combined data achieved the highest performance (AUC: 0.789, accuracy: 0.726, precision: 0.892).

Conclusions:

  • Combining structured and unstructured data enhances emergency severity prediction in ED patients.
  • Integrating text data into predictive models offers more accurate severity assessments, improving resource allocation and patient outcomes.
  • Future research should focus on real-time application and validation in diverse clinical settings.