Dar sentido a los datos | 10 ENE 21

El uso de las ontologías en la medicina de precisión

La presente revisión describe las ontologías y su uso en el razonamiento computacional para respaldar la clasificación precisa de los pacientes para el diagnóstico, la administración de la asistencia y la investigación traslacional
Autor/a: Haendel M, Chute C, Robinson P New England Journal of Medicine 379(15):1452-1462, Nov 2018
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Referencias

1. National Research Council, Committee on a Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press, 2011.

2. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008; 455: 1061-8.

3. Marx V. The DNA of a nation. Nature 2015; 524: 503-5.

4. Goroll AH. Emerging from EHR purgatory — moving from process to outcomes. N Engl J Med 2017; 376: 2004-6.

5. Hyman DM, Puzanov I, Subbiah V, et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N Engl J Med 2015; 373: 726-36. 6. Cornet R, Chute CG. Health concept and knowledge management: twenty-five years of evolution. Yearb Med Inform 2016; Suppl 1: S32-S41.

7. Nadkarni PM, Darer JA. Migrating existing clinical content from ICD-9 to SNOMED. J Am Med Inform Assoc 2010; 17: 602-7.

8. Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF, eds. The description logic handbook: theory, implementation, and applications. New York: Cambridge University Press, 2003 (https:// dl . acm . org/ citation . cfm?id=885746).

9. National Library of Medicine. SNOMED CT. 2016 (https://www . nlm . nih . gov/ healthit/ snomedct).

10. 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;18:441-8.

11. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004;32(Database issue):D267-D270.

12. Chute CG. Clinical classification and terminology: some history and current observations. J Am Med Inform Assoc 2000; 7:298-303.

13. Robinson PN, Bauer S. Introduction to biol-ontologies. Boca Raton, FL: CRC Press, 2011.

14. Rath A, Olry A, Dhombres F, Brandt MMC, Urbero B, Ayme S. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat 2012;33:803-8.

15. Köhler S, Vasilevsky NA, Engelstad M, et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res 2017;45(D1): D865-D876.

16. Hastings J, de Matos P, Dekker A, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2013;41(Database issue):D456-D463.

17. Bandrowski A, Brinkman R, Brochhausen M, et al. The Ontology for Biomedical Investigations. PLoS One 2016; 11(4):e0154556.

18. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res 2015;43(Database issue): D1049-D1056.

19. Fabregat A, Sidiropoulos K, Garapati P, et al. The Reactome pathway Knowledgebase. Nucleic Acids Res 2016;44(D1): D481-D487.

20. Pulteney R, Maton WG, Troilius C, von Linné C. A general view of the writings of Linnaeus. London: J. Mawman, 1805 (https://www.worldcat.org/title/general -view-of-the-writings-of-linnaeus/oclc/ 718424031&referer=brief_results).

21. Knibbs GH. The International classification of disease and causes of death and its revision. Med J Aust 1929;1:2- 12.

22. Rea S, Pathak J, Savova G, et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform 2012;45:763-71.

23. Pathak J, Bailey KR, Beebe CE, et al. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc 2013;20(e2):e341- e348.

24. Conway M, Berg RL, Carrell D, et al. Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms. AMIA Annu Symp Proc 2011;2011:274-83.

25. Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical recordbased phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013; 20(e1):e147-e154.

26. Pathak J, Wang J, Kashyap S, et al. Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience. J Am Med Inform Assoc 2011;18:376-86.

27. Smoller JW. The use of electronic health records for psychiatric phenotyping and genomics. Am J Med Genet B Neuropsychiatr Genet 2017 May 30 (Epub ahead of print).

28. Evans DA, Cimino JJ, Hersh WR, Huff SM, Bell DS. Toward a medical-concept representation language. J Am Med Inform Assoc 1994;1:207-17.

29. Campbell KE, Cohn SP, Chute CG, Rennels G, Shortliffe EH. Gálapagos: computer-based support for evolution of a convergent medical terminology. Proc AMIA Annu Fall Symp 1996:269-73.

30. Pourzanjani A, Quisel T, Foschini L. Adherent use of digital health trackers is associated with weight loss. PLoS One 2016;11(4):e0152504.

31. Eriksson N, Macpherson JM, Tung JY, et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet 2010;6(6): e1000993.

32. Vasilevsky NA, Foster ED, Engelstad ME, et al. Plain-language medical vocabulary for precision diagnosis. Nat Genet 2018;50:474-6.

33. Rauch A, Wieczorek D, Graf E, et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 2012;380:1674-82.

34. Yang Y, Muzny DM, Reid JG, et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med 2013;369:1502-11.

35. Yang Y, Muzny DM, Xia F, et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA 2014;312:1870-9.

36. Zhu X, Petrovski S, Xie P, et al. Wholeexome sequencing in undiagnosed genetic diseases: interpreting 119 trios. Genet Med 2015;17:774-81.

37. Dragojlovic N, Elliott AM, Adam S, et al. The cost and diagnostic yield of exome sequencing for children with suspected genetic disorders: a benchmarking study. Genet Med 2018 January 4 (Epub ahead of print).

 

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