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. 2014 Aug 15;9(1):206-11.
doi: 10.15265/IY-2014-0006.

EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group

L J Frey  1 , L Lenert, G Lopez-Campos
Affiliations

EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group

L J Frey et al. Yearb Med Inform. .

Abstract

Objectives: Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records.

Methods: As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships.

Conclusions: Stead and colleagues' 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.

Keywords: Deep phenotype; big data; electronic health record; genome; ontology.

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References

    1. Cases M, Fulong LI, Albanell J, Altman RB, Bellazzi R, Boyer S, et al. Improving data and knowledge management to better integrate health care and research. J Intern Med 2013:321–8. - PMC - PubMed
    1. Starren J, Williams MS, Bottinger EP. Crossing the omic chasm: a time for omic ancillary systems. JAMA 2013. Mar 27;309(12):1237–8. - PMC - PubMed
    1. Masys DR, Jarvik GP, Abernethy NF, Anderson NR, Papanicolaou GJ, Paltoo DN, et al. Technical desiderata for the integration of genomic data into Electronic Health Records. J Biomed Inform 2012. Jun;45(3):419–22. - PMC - PubMed
    1. Stead WW, Kelly BJ, Kolodner RM. Achievable Steps Toward Building a National Health Information Infrastructure in the United States. J Am Med Inform Assoc 2005;12(2):113–21. - PMC - PubMed
    1. Rabbani B, Mahdieh N, Hosomichi K, Nakaoka H, Inoue I. Next-generation sequencing: impact of exome sequencing in characterizing Mendelian disorders. J Hum Genet 2012. July:621–32. - PubMed

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