Predicting length of stay with administrative data from acute and emergency care: an embedding approach

**Vincent Lequertier**, Tao Wang, Julien Fondrevelle, Vincent Augusto, St├ęphanie Polazzi, Antoine Duclos


Hospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly.

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
August, 2021