A DSS for assessing the impact of environmental quality on emergency hospital admissions

In this paper we present a Decision Support System (DSS) aimed at forecasting high demand of admission on health care structures due to environmental pollution. The computational engine of the DSS is based on Autoregressive Hidden Markov Models (AHMM). We estimate the forecasting model by analyzing the historical daily average concentrations of pollutants and the number of hospital admissions, collected from multiple data sources. Given the actual concentration of different pollutants, measured by a network of sensors, the DSS allows to forecast the demand of hospital admissions for acute diseases in the following 1 to 6 days. We tested our system on cardiovascular and respiratory diseases in the area of Milan. The performances of our system, compared with multiple linear regression, show that AHMM are a robust approach to capture the connections among health and environmental indicators.

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