Big Data en salud: retos y oportunidades.

  1. Ernestina Menasalvas 1
  2. Consuelo Gonzalo 1
  3. Alejandro Rodríguez González 1
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

Revista:
Economía industrial

ISSN: 0422-2784

Año de publicación: 2017

Título del ejemplar: Nuevas tecnologías digitales

Número: 405

Páginas: 87-97

Tipo: Artículo

Otras publicaciones en: Economía industrial

Resumen

Las aplicaciones de Big Data en el sector de la salud presentan un alto potencial para mejorar la eficiencia y la calidad de la atención sanitaria. En este artículo se realiza un análisis a algunas de las iniciativas llevadas a cabo en este entorno enfatizando la cantidad de datos que se producen en los entornos sociales y la utilización tanto de las redes como de los datos puede suponer una gran diferencia en la aplicación del paradigma Big Data a la salud, que ha de abordar retos tecnológicos tales como: i) procesamiento de lenguaje natural, ii) implicaciones del análisis de datos provenientes de redes sociales, iii) interoperabilidad, iv) análisis de imágenes y v) confidencialidad y seguridad de los datos.

Referencias bibliográficas

  • AGGARWAL, C. C., HAN, J., WANG, J., & YU, P. S. (2003). «A Framework for Clustering Evolving Data Streams». In Proceedings of the 29th International Conference on Very Large Data Bases Volume 29 (pp. 81–92). Berlin, Germany: VLDB Endowment. Retrieved from http://dl.acm.org/citation. cfm?id=1315451.1315460
  • AGUILAR-RUIZ, J. S., & GAMA, J. (2005). «Data Streams». Journal of Universal Computer Science, 11(8), 1349–1352.
  • ASAMOAH, D., SHARDA, R., & KUMARASAMY, A. T. (2015). «Can Social Media Support Public Health? Demonstrating Disease Surveillance using Big Data Analytics». AMCIS 2015 Proceedings. Retrieved from http://aisel.aisnet.org/amcis2015/HealthIS/GeneralPresentations/12
  • BASEL KAYYALI, DAVID KNOTT, & STEVE VAN KUIKEN. (2013). «The big-data revolution in US health care: Accelerating value and innovation». In McKinsey Company. McKinsey Company. Retrieved from http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care
  • BENDER, J. L., JIMENEZ-MARROQUIN, M.-C., & JADAD, A. R. (2011). «Seeking Support on Facebook: A Content Analysis of Breast Cancer Groups». Journal of Medical Internet Research, 13(1). https://doi.org/10.2196/jmir.1560
  • BONNIE FELDMAN, ELLEN M. MARTIN, & TOBI SKOTNES. (2012). «Big Data in Healthcare Hype and Hope». Retrieved from http://www.west-info.eu/files/big-data-in-healthcare. pdf
  • CAPURRO, D., COLE, K., ECHAVARRÍA, M. I., JOE, J., NEOGI, T., & TURNER, A. M. (2014). «The Use of Social Networking Sites for Public Health Practice and Research: A Systematic Review». Journal of Medical Internet Research, 16(3), e79. https://doi.org/10.2196/jmir.2679
  • CUNNINGHAM, H. (2002). «GATE, a General Architecture for Text Engineering». Computers and the Humanities, 36(2), 223–254. https://doi.org/10.1023/A:1014348124664
  • DEY, N., KARÁ¢ A, W. B. A., CHAKRABORTY, S., BANERJEE, S., SALEM, M. A., & AZAR, A. T. (2015). «Image mining framework and techniques: a review». International Journal of Image Mining, 1(1), 45-64.
  • DHAWAN, A. P. (2013). «Medical Image Analysis», Volumen 31 de IEEE Press Series on Biomedical Engineering, John Wiley & Sons
  • DOMINGOS, P., & HULTEN, G. (2000). «Mining High-speed Data Streams». In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). New York, NY, USA: ACM. https://doi.org/10.1145/347090.347107
  • EVIKA KARAMAGIOLI. (2015). «Social media as a big public health data source: review of the international bibliography». PeerJ Preprint.
  • FAYYAD, U. M., PIATETSKY-SHAPIRO, G., & SMYTH, P. (1996). «From data mining to knowledge discovery: an overview». In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 1–34). Menlo Park, CA, USA: American Association for Artificial Intelligence. Retrieved from http://dl.acm. org/citation.cfm?id=257938.257942
  • FERNÁNDEZ BAIZÁN, C., MENASALVAS RUIZ, E., MARBÁN GALLEGO, Ó., & PEÑA SANCHEZ, J. M. (2001). «Minimal Decision Rules Based on the A Priori Algorithm». International Journal of Applied Mathematics and Computer Science, 11(3), 671–704.
  • FERRUCCI, D., & LALLY, A. (2004). UIMA: «An Architectural Approach to Unstructured Information Processing in the Corporate Research Environment». Nat. Lang. Eng., 10(3–4), 327–348. https://doi.org/10.1017/S1351324904003523
  • GABER, M. M., KRISHNASWAMY, S., & ZASLAVSKY, A. (2005). «On-board Mining of Data Streams in Sensor Networks». In Advanced Methods for Knowledge Discovery from Complex Data (pp. 307–335). Springer London. https://doi. org/10.1007/1-84628-284-5_12
  • IAN H. WITTEN, EIBE FRANK, & MARK A. HALL. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (3 edition). Burlington, MA: Morgan Kaufmann.
  • KASS-HOUT, T. A., & ALHINNAWI, H. (2013). «Social media in public health». British Medical Bulletin, 108(1), 5–24. https:// doi.org/10.1093/bmb/ldt028
  • LEIS, Á., MAYER, M. Á., TORRES NIÑO, J., RODRÍGUEZ-GONZÁLEZ, A., SUELVES, J. M., & ARMAYONES, M. (2013). «Grupos sobre alimentación saludable en Facebook: características y contenidos». Gaceta Sanitaria, 27(4), 355–357. https:// doi.org/10.1016/j.gaceta.2012.12.010
  • LOPER, E., & BIRD, S. (2002). NLTK: «The Natural Language Toolkit». In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics Volume 1 (pp. 63–70). Stroudsburg, PA, USA: Association for Computational Linguistics. https://doi.org/10.3115/1118108.1118117
  • MENASALVAS, E. & GONZALO-MARTÍN, C., (2016). Machine Learning for Health Informatics, Volume 9605 of the series Lecture Notes in Computer Science pp 221-242.
  • NAMBISAN, P., LUO, Z., KAPOOR, A., PATRICK, T. B., & CISLER, R. A. (2015). «Social Media, Big Data, and Public Health Informatics: Ruminating Behavior of Depression Revealed through Twitter». In 2015 48th Hawaii International Conference on System Sciences (HICSS) (pp. 2906–2913). https:// doi.org/10.1109/HICSS.2015.351
  • PADREZ, K. A., UNGAR, L., SCHWARTZ, H. A., SMITH, R. J., HILL, S., ANTANAVICIUS, T., … MERCHANT, R. M. (2015). «Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department». BMJ Quality & Safety, bmjqs-2015-004489. https://doi.org/10.1136/bmjqs-2015-004489
  • PAUL, M. J., & DREDZE, M. (2011). «You Are What You Tweet: Analyzing Twitter for Public Health». In Fifth International AAAI Conference on Weblogs and Social Media. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ ICWSM11/paper/view/2880
  • RODRÍGUEZ-GONZÁLEZ, A., MAYER, M. A., & FERNÁNDEZ-BREIS, J. T. (2013). «Biomedical information through the implementation of social media environments». Journal of Biomedical Informatics, 46(6), 955–956. https://doi.org/10.1016/j.jbi.2013.10.006
  • RODRÍGUEZ-GONZÁLEZ, A., RUIZ, E. M., & PUJADAS, M. A. M. (2016). «Automatic extraction and identification of users’ responses in Facebook medical quizzes». Computer Methods and Programs in Biomedicine, 127, 197–203. https:// doi.org/10.1016/j.cmpb.2015.12.025
  • SEGURA-BEDMAR, I., MARTÍNEZ, P., REVERT, R., & MORENO-SCHNEIDER, J. (2015). «Exploring Spanish health social media for detecting drug effects». BMC Medical Informatics and Decision Making, 15(Suppl 2), S6. https://doi.org/10.1186/1472-6947-15-S2-S6
  • SEIFERT, S., KELM, M., MOELLER, M., MUKHERJEE, S., CAVALLARO, A., HUBER, M., & COMANICIU, D. (2010, March). «Semantic annotation of medical images». In SPIE medical imaging (pp. 762808-762808). International Society for Optics and Photonics.
  • XIE, Y., CHEN, Z., CHENG, Y., ZHANG, K., AGRAWAL, A., LIAO, W.-K., & CHOUDHARY, A. (2013). «Detecting and Tracking Disease Outbreaks by Mining Social Media Data». In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (pp. 2958–2960). Beijing, China: AAAI Press. Retrieved from http://dl.acm.org/citation. cfm?id=2540128.2540556
  • ZHANG, D., ISLAM, M. M., & LU, G. (2012). «A review on automatic image annotation techniques». Pattern Recognition, 45(1), 346-362.