Meteorological factors for subarachnoid hemorrhage in the greater Düsseldorf area revisited: a machine learning approach to predict the probability of admission of patients with subarachnoid hemorrhage

Hans-Jakob Steiger,Athanasios K. Petridis,Angelo Tortora,Hendrik-Jan Mijderwijk,Kerim Beseoglu,Jasper H. van Lieshout,Marcel A. Kamp,Igor Fischer

Meteorological factors for subarachnoid hemorrhage in the greater Düsseldorf area revisited: a machine learning approach to predict the probability of admission of patients with subarachnoid hemorrhage

2019

Hans-Jakob Steiger
Athanasios K. Petridis
Angelo Tortora
Hendrik-Jan Mijderwijk
Kerim Beseoglu
Jasper H. van Lieshout
Marcel A. Kamp
Igor Fischer

Background Reported data regarding the relation between the incidence of spontaneous subarachnoid hemorrhage (SAH) and weather conditions are conflicting and do so far not allow prognostic models.

Keywords:

Subarachnoid hemorrhage
Medicine
Anesthesia
Spontaneous subarachnoid hemorrhage
learning methods
weak correlation
Poisson distribution
prognostic models
Pearson product-moment correlation coefficient
positive correlation
area under curve

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