The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France

Objective 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. Materials and Methods Aggregated data from
SARS-CoV-2 and weather public database and data warehouse of the Bordeaux
hospital were extracted from May 16, 2020, to January 17, 2022. 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. Results During the
period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at
the Bordeaux Hospital. 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 0.136 [0.063; 0.223] and 0.198
[0.105; 0.302] hospitalizations, respectively. Electronic health records
(EHRs) from the hospital data warehouse improved median relative error at
7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation
showed remaining forecast error was mainly due to delay in slope shift
detection. Discussion Forecast models showed overall good performance both
at 7 and 14 days which was improved by the addition of the data from
Bordeaux Hospital data warehouse. Conclusions The development of hospital
data warehouses might help to get more specific and faster information
than traditional surveillance systems, which in turn will help to improve
epidemic forecasting at a larger and finer scale.