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The Benefit of Augmenting Open Data with Clinical Data-Warehouse EHR for Forecasting SARS-CoV-2 Hospitalizations in Bordeaux Area, France

25 Pages Posted: 31 Mar 2022

See all articles by Thomas Ferté

Thomas Ferté

University of Bordeaux - CHU de Bordeaux

Vianney Jouhet

University of Bordeaux - CHU de Bordeaux

Romain Griffier

University of Bordeaux - CHU de Bordeaux

Boris P. Hejblum

University of Bordeaux - ISPED, INSERM U1219, Inria BSO

Rodolphe Thiébaut

University of Bordeaux - CHU de Bordeaux

Bordeaux University Hospital Covid-19 Crisis Task

Independent; University of Bordeaux - CHU de Bordeaux

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Abstract

Background: The ability to anticipate SARS-CoV-2 pandemic evolution and especially the number of hospitalizations in a short-time interval, is critical to better organize health care system. Several forecast models have been proposed relying on public data sources. In this work, we hypothesized that forecasts should be improved by the enrichment of the data from hospital data-warehouse including ambulance service and emergency units reports. The objective was to predict the number of hospitalized patients over one or two weeks in one of the main regional hospital in Southwestern France.

Methods: Aggregated data from SARS-CoV-2 and weather public database and data-warehouse of the Bordeaux hospital were extracted from 2020-05-16 to 2022-01-17. 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 including elastic-net penalized regressions, random forest and Fréchet random forest.

Findings: During the period of 88 weeks, 2561 hospitalizations due to COVID19 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 absolute error (MAE) at 7 and 14 days of 6·41 [6·07 ; 6·81] and 10·11 [9·54 ; 10·65] hospitalizations, respectively. Electronic health records from the hospital data-warehouse improved median absolute error at 7 and 14 days by around 17%. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection.

Interpretation: 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. However, the shift of the dynamic during each infection wave remained difficult to predict.

Funding: This work has been partly supported by Inria, Mission COVID19, GESTEPID project and Nouvelle Aquitaine regional funding (Prediction territorial COVID N°1333140).

Declaration of Interest: We declare no competing interests.

Ethical Approval: No ethics committee approval was needed for this work as data used for modelling were aggregated.

Keywords: SARS-CoV-2, forecasting, electronic health records, Data Warehouse, machine learning

Suggested Citation

Ferté, Thomas and Jouhet, Vianney and Griffier, Romain and Hejblum, Boris P. and Thiébaut, Rodolphe and crisis Task, Bordeaux University Hospital Covid-19, The Benefit of Augmenting Open Data with Clinical Data-Warehouse EHR for Forecasting SARS-CoV-2 Hospitalizations in Bordeaux Area, France. Available at SSRN: https://ssrn.com/abstract=4071506 or http://dx.doi.org/10.2139/ssrn.4071506

Thomas Ferté

University of Bordeaux - CHU de Bordeaux ( email )

Vianney Jouhet

University of Bordeaux - CHU de Bordeaux ( email )

Romain Griffier

University of Bordeaux - CHU de Bordeaux ( email )

Boris P. Hejblum

University of Bordeaux - ISPED, INSERM U1219, Inria BSO ( email )

France

Rodolphe Thiébaut (Contact Author)

University of Bordeaux - CHU de Bordeaux

University of Bordeaux - CHU de Bordeaux

Bordeaux
France

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