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 evolution of Big Data created the conditions for transforming the Healthcare industry towards the Electronic Health Records (EHR) and managing computerized archives. The EHR may contain the information about various attributes of the patients, which could include demographic details, previous medication history, and allergies, vaccination status, laboratory test results, EMR scan reports, payer information, insurer details, a previous visit to hospitals and so on @cite_32 .
@cite_32
1596659783
The term electronic health record (EHR) refers to the complete set of information that resides in electronic form and is related to the past, present, and future health status or health care provided to a subject of care. The primary purpose of electronic health records (EHRs) is the documentation, retrieval, transmission, linking, and processing of multimedia information to legitimate users for the delivery of knowledge and decision support that enhance efficient and secure health-related services, regardless of the health-care model applied. Secondary uses of EHRs are related to policy development, education, research, quality management, and disease surveillance via anonymized datasets requiring, in most of the cases, explicit consent on behalf of the subject of care. Keywords: electronic health record architecture; health information infrastructure; reference information model