A System for Predicting Hospital Admission at Emergency Department Based on Electronic Health Record Using Convolution Neural Network

Emergency Department (ED) crowding has become an issue of delayed patient treatment and even a public healthcare problem around the world. According to recent research studies of many countries, the increasing number of patients in the emergency department which has led to unprecedented crowding and delays in care. For that reason, triage into five-level Emergency Severity Index (ESI) has become a major method for improving medical priorities in ED. Although the ESI mitigates the process of ED treatment, so far it still heavily relies on the nurse's subjective judgment and is easy to triage most patients to ESI level 3 in current practice. Therefore, a system that can help the doctors to accurately triage a patient's condition is imperative. In this work, we propose a system based on the patients' ED electronic health record to predict hospitalizations after assigned procedures in ED are completed. While most of the related studies have employed traditional machine learning for triage-related classification and highly relied on a feature selection process, our proposed system used data-to-image transform to produce the input and a convolutional neural network as a classifier. For validation, the data from an open dataset (National Hospital Ambulatory Medical Care Survey) is used which includes 118,602 patient visits of United States EDs from 2012 to 2016 survey years. To sum up, the resulting AUROC and accuracy achieve 0.86 and 0.77, respectively, in our work.