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首页> 外文期刊>International journal of biomedical engineering and technology >Pharmacovigilance predictive analysis using NLP-based cloud
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Pharmacovigilance predictive analysis using NLP-based cloud

机译:基于NLP云的药物检测预测分析

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摘要

Nowadays, healthcare on Big data are a major research area in Computer Science field. This paper presents a mysterious analysis of pharmacovigilance from reviewers using NLP cloud environment. The historical and comparative methods upon doctors' prescription data and analysis are performed in the NLP (Natural Language Processing) cloud. For improving analysis of pharmacovigilance in medical research our system of approach not only explains the healthcare monitoring system but also scalable psychoanalysis of medical data. The system of approach explored by the variation of offline and online feedbacks of patients and it reveals through sentimental analysis in the NLP cloud system. Pharmacovigilance in NLP cloud analysis process identified the emotional analysis of the patient medicine intake data. The existing conventional methods of pharmacovigilance are taken upon clinical trials and small groups of tests data. The comparison result helps to find quicker analysis of medicine intake of the patients and protect from adverse drug event. Our approach will gain with the effort by the pharmacovigilance in cloud for patients. This innovation furnishes patients and specialists with openness to data that can enhance healthcare by investigating the primary as well as secondary data. A novelty approach which will make better service for tablets and pharma products in the medical field and as well as avoids overdosage and adverse effect event.
机译:如今,大数据的医疗保健是计算机科学领域的一个主要研究区域。本文介绍了使用NLP云环境的审阅者的药物检测的神秘分析。医生处方数据和分析的历史和比较方法是在NLP(自然语言处理)云中进行的。为了改善医学研究中的药物检测的分析我们的方法系统不仅解释了医疗保健监测系统,还解释了医疗数据的可扩展性心理分析。患者离线和在线反馈的变化探索的方法和揭示了NLP云系统中的敏感分析。 NLP云分析过程中的药物检测确定了患者药物进口数据的情绪分析。在临床试验和小型测试数据组上采取现有的常规药物方法。比较结果有助于寻找更快地分析患者的药物摄入量并保护免受药物的影响。我们的方法将在患者云中的药物群体中获得努力。这项创新提供患者和专家对可以通过调查初级和二级数据来增强医疗保健的数据的开放性。一种新颖的方法,将更好地为医疗领域的平板电脑和制药产品提供服务,并避免过量和不良反应事件。

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