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A Patient Oriented Framework using Big Data C-means Clustering for Biomedical Engineering Applications

机译:使用大数据和C均值聚类的面向患者的框架,用于生物医学工程应用

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Big data and Machine Learning have changed the healthcare research in recent years. Data generated from Electronic Health Records (EHRs) and other clinical sources now can be used further to help the patients. By applying Big Data Analytics (BDA) into healthcare data, it is possible to predict the outcome or the effects of drugs or risk of developing disease on human body. Several machine learning algorithms such as clustering, classification are used to analyze healthcare data. In this article, a framework is proposed using C-means Clustering for Biomedical Engineering applications. The framework can be used to help both the clinicians and the patients. For example, using this framework, a clinician can make a decision to prescribe suitable drug to a particular patient. In order to develop this framework, data has been collected from UCI machine learning repository. The data then analyzed using a well known big data framework Hadoop.
机译:大数据和机器学习改变了近年来的医疗保健研究。从电子健康记录(EHR)和其他临床来源生成的数据现在可以进一步用于帮助患者。通过将大数据分析(BDA)应用于医疗保健数据,可以预测药物的结果或作用或对人体产生疾病的风险。几种机器学习算法(例如聚类,分类)用于分析医疗保健数据。在本文中,提出了使用C均值聚类的生物医学工程应用程序框架。该框架可用于帮助临床医生和患者。例如,使用该框架,临床医生可以决定向特定患者开出合适的药物。为了开发此框架,已从UCI机器学习存储库中收集了数据。然后使用众所周知的大数据框架Hadoop分析数据。

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