A DSS for assessing the impact of environmental quality on emergency hospital admissions
Health Care Management（2010）
摘要
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.
更多查看译文
关键词
autoregressive processes,decision support systems,environmental factors,health care,hidden markov models,medical administrative data processing,ahmm,dss,dss computational engine,milan,autoregressive hidden markov models,cardiovascular disease,decision support system,emergency hospital admissions,environmental pollution,environmental quality,forecasting model,health care structures,historical daily average concentrations,multiple data sources,multiple linear regression,pollutants,respiratory diseases,health-environment correlation,hospital demand planning,time series analysis,data models,mathematical model,predictive models,hidden markov model
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要