使用深度学习方法诊断血液病

Tuba Karagül, Nilüfer Yurtay, Birgül Öneç
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引用次数: 0

摘要

确定疾病的诊断是治疗患者的重要步骤。此外,血液检查的数值,患者的个人信息,最重要的是,专家的意见是诊断疾病所必需的。随着技术的发展,患者相关数据的获取速度越来越快,数据量越来越大。通过处理原始数据产生有意义的结果的深度学习方法,现在开始给出接近人类观点的结果。目前的工作旨在开发一种系统,该系统将能够在一般情况下诊断贫血,因为患者数量和医院的意图不断增加,以及难以获得专家医疗顾问。这项工作的主要贡献是像医生一样用数据作为医生使用数据的方式进行诊断。数据集来源于医院实际环境,未对输入的患者数据进行数据增减、属性增减、约简、积分、imputation、转换、离散化等干预。原始医院数据被分类用于诊断贫血类型,通过使用深度学习算法,准确率达到84,97%。
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Diagnosing Hematological Disorders Using Deep Learning Method
Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.
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