MACHINE LEARNING IMPLEMENTATION FOR SMART HEALTH RECORDS: A DIGITAL CARRY CARD
MACHINE LEARNING IMPLEMENTATION FOR SMART HEALTH RECORDS: A DIGITAL CARRY CARD
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智能健康记录的机器学习实现：数字随身卡
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
V. Dutt
中科院分区：
文献类型：
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作者：
Abhishek Kumar;T. Sairam;V. Dutt
机器学习是一种高层次的方法，适用于这种现实世界场景中的任何类型的医疗保健实施。我们需要实施机器学习方法，以确定与患者健康状况相关的最佳预测值，还需要分析以前的健康记录。为此，我们需要维护一个存储库或仓库，在那里我们需要维护与患者及其治疗相关的数字数据。为此，在这篇文章中，我们提出了一个应用程序，使用它，我们可以使用一个数字卡，只能由医生，接待员和医院的其他部门使用的病人的健康记录。我们使用响应式Web应用程序实现了这种预测方法，并使用机器学习和Python等高级方法进行预测和统计分析。在本文中，我们将对我们的数据应用一些机器学习方法，并将找出治疗过程的最佳解决方案和患者数字记录的良好维护。我们维护的记录将按顺序排列，并且有可能在管理员权限下进一步修改数据。所有这些信息都以应用程序的形式存储，我们解释了机器学习在该应用程序中的使用过程以及预测模型的设计和实现。
Machine learning is a high-level approach for any kinds of health care implementation in this real-world scenario. We need to implement machine learning methodologies to identify the best-predicted values related to the patients in their respected health condition and also need to analyze the previous health records. For that, we need to maintain a repository or the warehouse where we need to maintain digital data related to the patients and their treatment. For that in this article, we are proposing an application using which we can maintain the health records of the patients using a digital card which can be used only by the doctor, receptionist and the respected other departments in the hospital. We implemented this prediction methodology using the responsive web application and the remaining things like predictions and the statistical analysis with the advanced methodologies like machine learning and python. In this article, we impose some machine learning methodologies on our data and will find out the optimal solution for the process of treatment and the good maintenance of the digital records of the patients. The records we maintain will be in sequential order and there is a chance of modification of the data in further times on admin privileges. All these kind of information was stored in the form of application and we explained the procedure of machine learning usage in this application and the prediction models design and implementation.