Chief complaint classification with recurrent neural networks | JoVE Visualize

Area of Science:

Public Health Informatics
Computational Epidemiology
Machine Learning in Healthcare

Background:

Syndromic surveillance systems monitor population health using emergency department data.
Current keyword-based methods for classifying unstructured text in these records are limited.
Automated classification using machine learning can enhance speed and accuracy in outbreak detection.

Purpose of the Study:

To implement and compare recurrent neural network (RNN) models (LSTM, GRU) against traditional bag-of-words classifiers (MNB, SVM) for syndromic surveillance.
To evaluate model performance in classifying emergency department records using discharge diagnoses and chief complaints.
To identify which conditions are most amenable to surveillance based on chief complaint data alone.

Main Methods:

Four machine learning models were implemented: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVM).
Models were trained on 3.6 million de-identified emergency department records to predict diagnostic code groups.
Performance was primarily assessed using the F1 score, comparing predictions from discharge diagnoses and chief complaints.

Main Results:

LSTM achieved the highest performance using discharge diagnoses (F1 score > 96.00), outperforming other models.
GRU demonstrated superior performance using chief complaints (F1 score = 47.38), while MNB with bigrams performed worst (F1 score = 39.40).
Certain conditions, like alcohol-related disorders, were predicted more accurately than others, such as influenza, highlighting variability in classification ease.

Conclusions:

Recurrent neural network models significantly outperform traditional bag-of-words classifiers for automated syndromic surveillance.
Deep learning approaches offer substantial improvements in classifying unstructured text data for public health monitoring.
Further research can optimize RNN models for more accurate and timely detection of health threats from emergency department chief complaints.