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.
personalized healthcare data mining patient-centered outcomes
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Conflict of Interest
The authors declare that they do not have a conflict of interest.
Chekhov AP., Matlaw, RE. Anton Chekhov’s Short Stories. WW Norton & Company Incorporated; 2008.Google Scholar
Engs RC. The Progressive Era’s Health Reform Movement: A Historical Dictionary. Praeger Publishers; 2003.Google Scholar
Davis DA, Chawla NV. Exploring and exploiting disease interactions from multi-relational gene and phenotype networks. PLoS One. 2011;6(7):e22670.PubMedCrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.
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.Google Scholar
Goldberg K, Roeder T, Gupta D, et al. A constant time collaborative filtering algorithm. Inf Retr. 2001;4(2):133–151.CrossRefGoogle Scholar
Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS). 2004;22(1):5–53.CrossRefGoogle Scholar
Hofmann T. Latent semantic models for collaborative filtering. ACM Trans Inf Syst (TOIS). 2004;22(1):89–115.CrossRefGoogle Scholar
Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Adv Artif Intell. 2009;4:421–425.Google Scholar
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.PubMedCrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.PubMedCrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.PubMedCrossRefGoogle Scholar
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.Google Scholar