title: The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network.
abstract: BACKGROUND
  Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network.
  
  
  METHODS
  A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study.
  
  
  RESULTS
  Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study.
  
  
  CONCLUSIONS
  The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.
authors.affiliation: Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Laleh
authors.initials: L
authors.lastname: Agharezaei
authors.affiliation: Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Zhila
authors.initials: Z
authors.lastname: Agharezaei
authors.affiliation: Department of Internal Medicine, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Nemati
authors.affiliation: Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Kambiz
authors.initials: K
authors.lastname: Bahaadinbeigy
authors.affiliation: Department of Power Engineering Graduate, University of Advanced Technology Kerman, Kerman, Iran.
authors.forename: Farshid
authors.initials: F
authors.lastname: Keynia
authors.affiliation: Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Baneshi
authors.affiliation: Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Abedin
authors.initials: A
authors.lastname: Iranpour
authors.affiliation: Department of IT and Computer Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran.
authors.forename: Moslem
authors.initials: M
authors.lastname: Agharezaei
keywords: Artificial Neural Network; Deep Vein Thrombosis; Pulmonary Embolism; Risk Factors; Venous thromboembolism