Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text

Arnaud, Emilien; Elbattah, Mahmoud; Gignon, Maxime; Dequen, Gilles

Abstract

Overcrowding in Emergency Departments (ED) is considered as an international issue, which could have adverse impacts on multiple care outcomes such as the length of stay for example. Part of the solution could lie in the early prediction of the patient outcome as discharge or hospitalization. This study applies Deep Learning to this end. A large-scale dataset of about 260K ED records was provided by the Amiens-Picardy University Hospital in France. In general, our approach is based on integrating structured data with unstructured textual notes recorded at the triage stage. The key idea is to apply a multi-input of mixed data for training a classification model to predict hospitalization. In a simultaneous manner, the model training utilizes the numeric features along with textual data. On one hand, a standard Multi-Layer Perceptron (MLP) model is used with the standard set of features (i.e. numeric and categorical). On the other hand, a Convolutional Neural Network (CNN) is used to operate over the textual data. The two components of learning are conducted independently in parallel. The empirical results demonstrated that the classifier could achieve a very good accuracy with ROC-AUC≈0.83. The study is conceived to contribute to the mounting efforts of applying Natural Language Processing in the healthcare domain.

Citation

Arnaud, E., Elbattah, M., Gignon, M., & Dequen, G. (2020). Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text. In 2020 IEEE International Conference on Big Data (Big Data) (4836-4841). https://doi.org/10.1109/bigdata50022.2020.9378073

• Conference Name: 2020 IEEE International Conference on Big Data (Big Data)
• Conference Location: Atlanta, GA, USA
• Start Date: Dec 10, 2020
• End Date: Dec 13, 2020
• Acceptance Date: Oct 16, 2020
• Online Publication Date: Mar 19, 2021
• Publication Date: Dec 10, 2020
• Deposit Date: Apr 26, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 4836-4841
• Book Title: 2020 IEEE International Conference on Big Data (Big Data)
• ISBN: 9781728162522
• DOI: https://doi.org/10.1109/bigdata50022.2020.9378073
• Public URL: https://uwe-repository.worktribe.com/output/9206302