title: Meteorological Factors Related to Emergency Admission of Elderly Stroke Patients in Shanghai: Analysis with a Multilayer Perceptron Neural Network.
abstract: BACKGROUND
  The aim of this study was to predict the emergency admission of elderly stroke patients in Shanghai by using a multilayer perceptron (MLP) neural network.
  
  
  METHODS
  Patients (>60 years) with first-ever stroke registered in the Emergency Center of Neurology Department, Shanghai Tenth People's Hospital, from January 2012 to June 2014 were enrolled into the present study. Daily climate records were obtained from the National Meteorological Office. MLP was used to model the daily emergency admission into the neurology department with meteorological factors such as wind level, weather type, daily maximum temperature, lowest temperature, average temperature, and absolute temperature difference. The relationships of meteorological factors with the emergency admission due to stroke were analyzed in an MLP model.
  
  
  RESULTS
  In 886 days, 2180 first-onset elderly stroke patients were enrolled, and the average number of stroke patients was 2.46 per day. MLP was used to establish a model for the prediction of dates with low stroke admission (≤4) and those with high stroke admission (≥5). For the days with low stroke admission, the absolute temperature difference accounted for 40.7% of admissions, while for the days with high stroke admission, the weather types accounted for 73.3%.
  
  
  CONCLUSIONS
  Outdoor temperature and related meteorological parameters are associated with stroke attack. The absolute temperature difference and the weather types have adverse effects on stroke. Further study is needed to determine if other meteorological factors such as pollutants also play important roles in stroke attack.
authors.affiliation: Department of Neurology, Tenth People's Hospital, Tongji University, Shanghai, China (mainland).
authors.forename: Guilin
authors.initials: G
authors.lastname: Meng
authors.affiliation: Department of Neurology, Tenth People's Hospital, Tongji University, Shanghai, China (mainland).
authors.forename: Yan
authors.initials: Y
authors.lastname: Tan
authors.affiliation: Department of Neurology, Tenth People's Hospital, Tongji University, Shanghai, China (mainland).
authors.forename: Min
authors.initials: M
authors.lastname: Fang
authors.affiliation: Department of Neurology, Tenth People's Hospital, Tongji University, Shanghai, China (mainland).
authors.forename: Hongyan
authors.initials: H
authors.lastname: Yang
authors.affiliation: Department of Neurology, Tenth People's Hospital, Tongji University, Shanghai, China (mainland).
authors.forename: Xueyuan
authors.initials: X
authors.lastname: Liu
authors.affiliation: Department of Neurology, Tenth People's Hospital, Tongji University, Shanghai, China (mainland).
authors.forename: Yanxin
authors.initials: Y
authors.lastname: Zhao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002681:China / Q000453:epidemiology; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008685:Meteorological Concepts; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology*; D013696:Temperature; D014887:Weather