ArticleComputational and mathematical methods in medicine2017
Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Review
- Psychosocial Factors and Psychological Characteristics of Personality of Patients with Chronic Diseases Using Artificial Intelligence Data Mining Technology and Wireless Network Cloud Service Platform.Computational intelligence and neuroscience · 2022Article
- Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.Journal of medical systems · 2021Review
- Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.NPJ digital medicine · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges of data aggregation, maintenance, integration, translation, analysis, and security/privacy. The study objective to establish an interactive BDA platform with simulated patient data using open-source software technologies was achieved by construction of a platform framework with Hadoop Distributed File System (HDFS) using HBase (key-value NoSQL database). Distributed data structures were generated from benchmarked hospital-specific metadata of nine billion patient records. At optimized iteration, HDFS ingestion of HFiles to HBase store files revealed sustained availability over hundreds of iterations; however, to complete MapReduce to HBase required a week (for 10 TB) and a month for three billion (30 TB) indexed patient records, respectively. Found inconsistencies of MapReduce limited the capacity to generate and replicate data efficiently. Apache Spark and Drill showed high performance with high usability for technical support but poor usability for clinical services. Hospital system based on patient-centric data was challenging in using HBase, whereby not all data profiles were fully integrated with the complex patient-to-hospital relationships. However, we recommend using HBase to achieve secured patient data while querying entire hospital volumes in a simplified clinical event model across clinical services.
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Registered trials
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