Agharezaei Laleh, Agharezaei Zhila, Nemati Ali, Bahaadinbeigy Kambiz, Keynia Farshid, Baneshi Mohammad Reza, Iranpour Abedin, Agharezaei Moslem
Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Acta Inform Med. 2016 Oct;24(5):354-359. doi: 10.5455/aim.2016.24.354.359. Epub 2016 Nov 1.
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.
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.
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.
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.
静脉血栓栓塞是住院患者死亡的常见原因，然而通过检测诱发因素并由专家进行及时诊断，它是可以预防的。本研究旨在借助人工神经网络协助专家对患者肺栓塞的风险水平进行诊断和预测。
本研究使用了31个风险因素来评估克尔曼医科大学附属的3家教学医院中294名住院患者的情况。本研究比较了两种类型的人工神经网络，即前馈反向传播网络和埃尔曼反向传播网络。
通过优化的人工神经网络模型，准确率和风险水平指数达到了93.23%，随后将结果与患者灌注扫描的结果进行了比较。通过灌注扫描诊断方法诊断出的86.61%的高风险患者也通过本研究提出的方法被正确诊断。
本研究结果可为医生、医疗助理和医护人员更准确地诊断高风险患者并预防死亡提供良好资源。此外，还可有效减少费用以及诸如灌注扫描等其他不必要的诊断方法。
