The personalized health care service utilizes the relational patient data and big data analytics to tailor the medication recommendations. However, most of the health care data are in unstructured form and it consumes a lot of time and effort to pull them into relational form. This study proposes a novel data lake architecture to reduce the data ingestion time and improve the precision of healthcare analytics. It also removes the data silos and enhances the analytics by allowing the connectivity to the third-party data providers (such as clinical lab results, chemist, insurance company,etc.). The data lake architecture uses the Hadoop Distributed File System (HDFS) to provide the storage for both structured and unstructured data. This study uses K-means clustering algorithm to find the patient clusters with similar health conditions. Subsequently, it employs a support vector machine to find the most successful healthcare recommendations for the each cluster. Our experiment results demonstrate the ability of data lake to reduce the time for ingesting data from various data vendors regardless of its format. Moreover, it is evident that the data lake poses the potential to generate clusters of patients more precisely than the existing approaches. It is obvious that the data lake provides a unified storage location for the data in its native format. It can also improve the personalized healthcare medication recommendations by removing the data silos.
The healthcare organisations create structured, semi-structured and unstructured data in the form of EHR. Even more importantly, only 20 Naturally, the sources for the data also range from mobile devices, social media feeds wearable devices, Radio Frequency Identification (RFID) devices, sensors and monitoring devices attached to the patient and their bed @cite_5 . Even though the health IT has seen tremendous technology development, it is challenging to manage this complex data flood.
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The digitization of medical data, field of genomics and use of wearable sensors to monitor patient health are some of the factors that have dramatically increased the growth of Big Data in Health Care Biomedicine. Big data in healthcare actually refers to electronic health data sets which are large and complex that is very difficult to manage with traditional conventional data management tools and techniques. Big data analytics in healthcare is cumbersome not just because of its volume but also because of the diversity of data types and the speed at which it is generated and must be managed analyzed. Rapid progress is to be made for analyzing this data and for gleaning new insights for making better informed decisions. There are unprecedented opportunities to use big data. The Health Care Industry should find methods to properly analyze this Big HealthCare Data generated and stored around the world each seconds in order to discover associations, understand the patterns and trends which will provide significant opportunities for real-time tracking of diseases, predicting disease outbreaks, to improve care, save lives and lower costs. Extraction, integration and analysis of heterogeneous, enormous and complex HealthCare data captured from various Electronic Health Care sources are a major challenge. New methods, applications and tools that are used by Healthcare industries, practitioners and researchers to tackle the big data challenges are discussed in this paper.