EHR Big Data Deep Phenotyping

Summary

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.

 

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Keywords

Deep phenotype - ontology - big data - genome - electronic health record

 

 

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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.
• 2 Starren J, Williams MS, Bottinger EP. Crossing the omic chasm: a time for omic ancillary systems.. JAMA 2013; Mar 27; 309 (12) 1237-8.
• 3 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 (03) 419-22.
• 4 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 (02) 113-21.
• 5 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.
• 6 Robinson PN. Deep Phenotyping for Precision Medicine.. Hum Mutat 2012; 33 (05) 777-80.
• 7 Dawkins R. The Extended Phenotype.. Oxford: Oxford University Press; 1989
• 8 Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records.. J Am Med Inform Assoc 2013; 20 (01) 117-21.
• 9 Darvasi A. Experimental strategies for the genetic dissection of complex traits in animal models.. Nat Genet 1998; 18: 19-24.
• 10 Doelken SC, Mungall CJ, Bauer S, Firth HV, Bail-leul-Forestier I, Black GCM. et al. The Human Phenotype Ontology project : linking molecular biology and disease through phenotype data.. Nucleic Acids Res 2013; 1-9.
• 11 Girdea M, Dumitriu S, Fiume M, Bowdin S, Boycott KM, Chitayat D. et al. PhenoTips: Patient Phenotyping Software for Clinical and Research Use.. Hum Mutat 2013; 34 (08) 1057-65.
• 12 Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology.. Nucleic Acids Res 32 (Database issue). 2004; 267-70.
• 13 Bodenreider O, Burgun A. Aligning knowledge sources in the UMLS: methods, quantitative results, and applications.. Medinfo Proc 2004; 327-31.
• 14 Wilson PS, Scichilone RA. LOINC as a data standard: how LOINC can be used in electronic environments.. J AHMIA 2011; 82 (07) 44-47.
• 15 Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years.. J Am Med Inform Assoc 2011; 8: 441-8.
• 16 International Classification of Diseases version 9.. [15 December 2013];. http://www.icd9data.com/2006/Volume1/
• 17 Melton GB, Parsons S, Morrison FP, Rothschild AS, Markatou M, Hripcsak G. Inter-patient distance metrics using SNOMED CT defining relationships.. J Biomed Inform 2006; 39: 697-705.
• 18 Robinson PN, Mundlos S. The Human Phenotype Ontology.. Clin Genet 2010; 77: 525-34.
• 19 McKusick VA. Mendelian Inheritance in Man.. A Catalog of Human Genes and Genetic Disorders. Baltimore:: Johns Hopkins University Press;; 1998. (12th edition).
• 20 Köhler S, Schulz MH, Krawitz P, Bauer S, Dölken S, Ott CE. et al. Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies.. Am J Hum Genet 2009; 85: 457-64.
• 21 Hajihashemi Z, Popescu M. An Early Illness Recognition Framework Using a Temporal Smith Waterman Algorithm and NLP.. Proc AMIA Fall Symp 2013; 549-57.
• 22 Wang TD, Plaisant C. et al. Aligning Temporal Data by Sentinel Events: Discovering Patterns in Electronic Health Records.. CHI 2008 Proceedings: Health and Wellness,; Florence, Italy;: 2008
• 23 Wongsuphasawat K, Gomez JAG. et al. LifeFlow: Visualizing an Overview of Event Sequences.. CHI 2011,; Vancouver,; BC, Canada;: 2011
• 24 Wongsuphasawat K, Gotz DH. Outflow: Visualizing Patient Flow by Symptoms and Outcome.. IBM;; 2011. p. 1-4.
• 25 Lee WN, Das AK. Local Alignment Tool for Clinical History: Temporal Semantic Search of Clinical Databases.. AMIA Annu Symp Proc 2010; 437-41.
• 26 Lee WN, Bridewell W, Das AK. Alignment and Clustering of Breast Cancer Patients by Longitudinal Treatment History.. AMIA Annu Symp Proc 2011; 2011: 760-7.
• 27 Ayres J, Gehrke J, Yiu T, Flannick J. Sequential Pattern Mining using a Bitmap Representation.. In: SIGKDD’02,; Edmonton,; Canada;: 2002. July.
• 28 Papapetrou P, Kollios G, Sclaroff S, Gunopulos D. Mining frequent arrangements of temporal intervals.. Knowl Inf Syst 2009; 21 (02) 133-71.
• 29 Shahar Y, Goren-Bar D, Boaz D, Tahan G. Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data.. Artif Intell Med 2006; 38 (02) 115-35.
• 30 Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM. et al. Exome sequencing identifies the cause of a mendelian disorder.. Nat Genet 2010; Jan 42 (01) 30-5.
• 31 Reese MG, Moore B, Batchelor C, Salas F, Cunningham F, Marth GT. et al. A standard variation file format for human genome sequences.. Genome Biol 2010; 11 (08) R88.
• 32 McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB. et al. The eMERGE Network : A consortium of biorepositories linked to electronic medical records data for conducting genomic studies.. BMC Med Genomics 2011; 4 (01) 13.
• 33 Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V. et al. Validation of electronic medical record-based phenotyping algorithms : results and lessons learned from the eMERGE network.. Medical Informatics 2013; 20: e147-e154.
• 34 Ghemawat S, Gobioff H. et al. The Google File System.. SOSP’03, Bolton Landing,; New York, USA;: 2003
• 35 Chang F, Dean J. et al. Bigtable: A Distributed Storage System for Structured Data.. OSDI ‘06: 7th USENIX Symposium on Operating Systems Design and Implementation;; 2006. p. 205-18.
• 36 Borthakur D, Sarma JS. et al. Apache Hadoop Goes Realtime at Facebook.. SIGMOD ‘11, Athens,; Greece.: 2011
• 37 Seoane JA, Dorado J, Pazos A. Data Integration in Genomic Medicine: Trends and Applications.. IMIA Yearbook 2012: Personal Health Informatics 2012; 117-25.