Use of Hidden Markov Models to Identify Background States Behind Risks of Cerebral Infarction and Ischemic Heart Disease

Abstract

Cold exposure is often said to trigger the incidence of cerebral infarctions and ischemic heart disease. This association between weather and human health has attracted considerable interest, and has been explored using standard statistical techniques such as regression models. Meteorological factors, such as temperature, are controlled by background systems, notably weather patterns. Therefore, it is reasonable to posit that the incidence of diseases is similarly influenced by a background system. The aim of this paper was to identify and construct these respective background systems. Possible background states or "hidden states", behind the incidence of diseases were derived using the EM and Viterbi algorithms with in the framework of hidden Markov models (HMM). A self-organizing map (SOM) enabled identification of weather patterns, considered as background states behind meteorological factors. These background states were then compared, and the hidden states behind the incidence of diseases were identified by six weather patterns. This finding indicates new evidence of the links between weather and human health, shedding light on the association between changes in the weather and the onset of disease.

Interleukins as a Common Indicator for the Effects of Both Weather and Obesity on Human Health

“…The influences of weather on the onset of stroke and ischemic heart disease were studied mostly by linear regression models [1,2] and recently by hidden Marokov models [3,4]. The effects of obesity and metabolic syndrome were also discussed as an important factor for the incidences of these diseases [5,6].…”

“…Recently, methods of data mining were applied to this issue. For example, a hidden Markov model was implemented and hidden states of weather associated with the onset of diseases were identified [3,4].…”