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Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework

ABSTRACT

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician’s office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan? These are the goals and vision of the work discussed in this paper. The timing is right for such a research direction—given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.

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REFERENCES

  1. 1.

    Chekhov AP., Matlaw, RE. Anton Chekhov’s Short Stories. WW Norton & Company Incorporated; 2008.

  2. 2.

    Engs RC. The Progressive Era’s Health Reform Movement: A Historical Dictionary. Praeger Publishers; 2003.

  3. 3.

    Ampath. http://www.ampathkenya.org/. Accessed 2012 Dec 20.

  4. 4.

    Kitano H. Systems biology: a brief overview. Science. 2002;295(5560):1662–1664.

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Burton PR, Clayton DG, Cardon LR, et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–678.

    Article  CAS  Google Scholar 

  6. 6.

    Loscalzo J. Association studies in an era of too much information. Circulation. 2007;116(17):1866–1870.

    PubMed  Article  Google Scholar 

  7. 7.

    Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol. 2007;3(1).

  8. 8.

    Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461(7261):218–223.

    PubMed  Article  CAS  Google Scholar 

  9. 9.

    Davis DA, Chawla NV. Exploring and exploiting disease interactions from multi-relational gene and phenotype networks. PLoS One. 2011;6(7):e22670.

    PubMed  Article  CAS  Google Scholar 

  10. 10.

    Davis DA, Chawla NV, et al. Predicting individual disease risk based on medical history. In Proceedings of the 17th ACM conference on Information and knowledge management. 2008:769–778. ACM.

  11. 11.

    Davis DA, Chawla NV, Christakis NA, Barabási AL. Time to CARE: a collaborative engine for practical disease prediction. Data Min Knowl Disc. 2010;20(3):388–415.

    Article  Google Scholar 

  12. 12.

    Dentino B, Davis D, Chawla NV. HealthCareND: leveraging EHR and CARE for prospective healthcare. In Proceedings of the 1st ACM International Health Informatics Symposium. 2010:841–844. ACM.

  13. 13.

    Stanton MW. Expanding patient-centered care to empower patients and assist providers. Research in Action. Agency for Healthcare Research and Quality, Rockville; 2002. Available: http://www.ahrq.gov/qual/ptcareria.htm.

  14. 14.

    Barabási AL. Network medicine—from obesity to the “diseasome”. N Engl J Med. 2007;357(4):404–407.

    PubMed  Article  Google Scholar 

  15. 15.

    Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. 1998: 43–52. Morgan Kaufmann Publishers Inc.

  16. 16.

    Goldberg K, Roeder T, Gupta D, et al. A constant time collaborative filtering algorithm. Inf Retr. 2001;4(2):133–151.

    Article  Google Scholar 

  17. 17.

    Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS). 2004;22(1):5–53.

    Article  Google Scholar 

  18. 18.

    Hofmann T. Latent semantic models for collaborative filtering. ACM Trans Inf Syst (TOIS). 2004;22(1):89–115.

    Article  Google Scholar 

  19. 19.

    Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Adv Artif Intell. 2009;4:421–425.

  20. 20.

    Starfield B, Lemke KW, Bernhardt T, et al. Comorbidity: implications for the importance of primary care in ‘case’management. Ann Fam Med. 2003;1(1):8–14.

    PubMed  Article  Google Scholar 

  21. 21.

    Wong DT, Knaus WA. Predicting outcome in critical care: the current status of the APACHE prognostic scoring system. Can J Anesth Can J Anesth. 1991;38(3):374–383.

    Article  CAS  Google Scholar 

  22. 22.

    Jenkins KJ, Gauvreau K, Newburger K, et al. Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg. 2002;123(1):110–118.

    PubMed  Article  Google Scholar 

  23. 23.

    Clamp SE, Myren J, Bouchier IA, Watkinson G, de Dombal FT. Diagnosis of inflammatory bowel disease: an international multicentre scoring system. Br Med J (Clin Res Ed). 1982;284(6309):91.

    Article  CAS  Google Scholar 

  24. 24.

    Piscaglia F, Cucchetti A, Benlloch S, et al. Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors. Eur J Gastroenterol Hepatol. 2006;18(12):1255–1261.

    PubMed  Article  Google Scholar 

  25. 25.

    Liu YH, Chen SM, Lin CY, et al. Motion tracking on elbow tissue from ultrasonic image sequence for patients with lateral epicondylitis. In 29th Annual International Conference of the IEEE. 2007:95–98.

  26. 26.

    Mould RF. Prediction of long-term survival rates of cancer patients. Lancet. 2003;361(9353):262.

    PubMed  Article  Google Scholar 

  27. 27.

    Snyderman R, Langheier J. Prospective health care: the second transformation of medicine. Genome Biol. 2006;7(2):104.

    PubMed  Article  Google Scholar 

  28. 28.

    Christakis NA, Allison PD. Mortality after the hospitalization of a spouse. N Engl J Med. 2006;354(7):719–730.

    PubMed  Article  CAS  Google Scholar 

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Conflict of Interest

The authors declare that they do not have a conflict of interest.

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Correspondence to Nitesh V. Chawla PhD.

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Chawla, N.V., Davis, D.A. Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework. J GEN INTERN MED 28, 660–665 (2013). https://doi.org/10.1007/s11606-013-2455-8

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KEY WORDS

  • personalized healthcare
  • data mining
  • patient-centered outcomes