Official websites use. Share sensitive information only on official, secure websites. The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records EHR information to perform those predictions.
The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.
Electronic health records EHRs from the hospital data warehouse improved median relative error at 7 and 14 days by Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection.
Forecast model showed overall good performance both at 7 and 14 days which were improved by the addition of the data from Bordeaux Hospital data warehouse. The development of hospital data warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale. To achieve this goal, several forecasting algorithms have been proposed.
Cramer et al 8 compared different approaches, including regression, compartmental, ensemble, deep-learning, to forecast the number of death related to COVID in the United States. Best models included ensemble, deep learning, and several compartmental methods.
