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Machine Learning-Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model

Kuan-Chen Chin1, Yu-Chia Cheng2, Jen-Tang Sun3

  • 1Department of Emergency Medicine, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan.

Journal of Medical Internet Research
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

A new machine learning model can help identify severely injured patients from road accidents using emergency call data. While not superior to human dispatchers, it aids their judgment in uncertain cases.

Keywords:
Bernoulli naïve Bayesdispatcheremergency medical dispatchemergency medical servicefrequency–inverse document frequencymachine learningtrauma

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Area of Science:

  • Emergency medicine
  • Artificial intelligence in healthcare
  • Public health informatics

Background:

  • Early recognition of severely injured patients is critical for timely prehospital treatment and transport.
  • Dispatching accuracy in emergency medical services has been understudied.
  • Accurate identification of major trauma patients post-road accident is essential.

Purpose of the Study:

  • To develop a machine learning model for automated identification of severely injured patients from road accident emergency calls.
  • To compare the performance of the machine learning model against human emergency medical dispatchers.
  • To enhance the accuracy of prehospital trauma assessment through text mining of emergency calls.

Main Methods:

  • A prehospital-activated major trauma (PAMT) model was developed using text mining (TF-IDF, rule-based classification, Bernoulli Naive Bayes) on emergency call audio recordings from road accidents.
  • Data from 2018 Taipei City road accidents were sampled, excluding call transfers and non-Mandarin calls.
  • The PAMT model's predictions were compared against 6 human dispatchers using metrics like sensitivity, specificity, and accuracy via cross-validation.

Main Results:

  • The PAMT model demonstrated higher mean sensitivity and negative predictive value compared to human dispatchers.
  • Human dispatchers achieved higher mean specificity, positive predictive value, and overall accuracy in most cases.
  • The PAMT model showed improved accuracy in situations where human dispatchers exhibited lower certainty.

Conclusions:

  • A machine learning model (PAMT) was successfully developed for predicting severe road accident trauma from emergency calls.
  • The PAMT model's accuracy was comparable to, but not superior to, human dispatchers.
  • The PAMT model can serve as a valuable tool to support emergency dispatchers, particularly in cases of diagnostic uncertainty.