Journal Article10.2307/2290162
Design, data & analysis
9
TL;DR: This project will create an Azure account that uses Data Lake, store electronic medical records / electronic health records (EMR / EHR) for patients, and use machine learning to classify EHR data types to retrieve and download the data stored in the data lake.
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Abstract: The data lake was built from scratch for the size and performance of the cloud. The Azure Data Lake Store allows any company to analyze all their data in one place without any human restrictions. A data lake store can store trillions of files, and one file is 200 times larger than other cloud storages. This means that there is no need rewrite the code as the size of the stored data increases or decreases or the amount of processing power spun up increases. Data Lake also removes the complexity normally associated with big data in the cloud, enabling it to meet current and future business needs. In this project, we will create an Azure account that uses Data Lake, store electronic medical records / electronic health records (EMR / EHR) for patients, and use machine learning to classify (EMR / EHR) data types. increase. Here the KNN algorithm is used for classification to retrieve and download the data stored in the data lake. Usercan also delete the data if the user no longer needs to change the data or system.
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TL;DR: This paper will provide psychophysiological researchers with recommendations and practical advice concerning experimental designs, data analysis, and data reporting to ensure that researchers starting a project with HRV and cardiac vagal tone are well informed regarding methodological considerations in order for their findings to contribute to knowledge advancement in their field.
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TL;DR: This document advocates for a multidisciplinary team of experts to guide institutional use of this therapy and the care of patients receiving it, and highlights key aspects of care delivery in this rapidly growing technology.
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TL;DR: The combination of Web Application Programming Interface and Semantic Web technologies has the potential to cope with the challenges of the challenges faced by the current healthcare system based on the analysis of the review result.
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Linear and non-linear analysis of cardiac health in diabetic subjects
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