Predicting individual disease risk based on medical history

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 769-778

  Davis, Darcy A ^a   Chawla, Nitesh V ^a   Blumm, Nicholas ^b   Barabási, Albert László ^b   Christakis, Nicholas ^c  

^a UNIVERSITY OF NOTRE DAME   (United States)

^b NORTHEASTERN UNIVERSITY   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Collaborative filltering; Disease risk prediction; Ensemble; Pospective health care

Indexed keywords

CHRONIC DISEASE; COLLABORATIVE FILLTERING; DATA SETS; DISEASE RISK PREDICTION; DISEASE RISKS; ENSEMBLE; MEDICAL CONDITIONS; MEDICAL HISTORY; POSPECTIVE HEALTH CARE; PREVENTATIVE MEDICINES; RECOMMENDATION ENGINE;

DISEASES; HEALTH; HEALTH CARE; KNOWLEDGE MANAGEMENT;

HEALTH RISKS;

EID: 70349260874     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458082.1458185     Document Type: Conference Paper

References (16)

1. A.-L. Barabasi. Network medicine - from obesity to the dieaseasome. New England Journal of Medicine, 357:404-407, 2007.
2. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. Technical Report MSR-TR-98-12, Microsoft Research, May 1998.
3. D. K. Cherry, C. W. Burt, and D. Woodwell. A national ambulatory medical care survey: 2001 summary. Advance Data, 337:1-16, 2001.
4. W. T. C. Consortium. A national ambulatory medical care survey: 2001 summary. Nature, 447:661-678, 2007.
5. O. Cordón, F. Herrera, J. de la Montana, A. Sánchez, and P. Villar. A prediction system for cardiovascularity diseases using genetic fuzzy rule-based systems. In Proceedings of the 8th Ibero-American Conference on AI, pages 381-391. Springer Berlin, 2002.
6. N. C. for Health Statistics. International Classification of Diseases, Ninth Revision, Clinical Modification (icd-9-cm), 2007. http://www.cdc.gov/nchs/ about/otheract/icd9/.
7. D. Heckerman, D. M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering, and data visualization. Technical Report MSR-TR-2000-16, Microsoft Research, February 2001. [8] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22:5-53, 2004.
8. J. Langheier and R. Snyderman. Prospective medicine: The role of genomics in personalized health planning. Pharmacogenomics, pages 1-8, 2004.
9. D. S. Lauderdale, S. E. Furner, T. P. Miles, and J. Goldberg. Epidemiologic uses of medicare data. Epidemiologic Reviews, 15:319-27, 1993.
10. Y. Liu, L. Teverovskiy, O. Lopez, H. Aizenstein, C. Meltzer, and J. Becker. Discovery of biomarkers for alzheimer's disease prediction from structural mr images. In 2007 IEEE International Symposium on Biomedical Imaging, April 2007.
11. J. Loscalzo. Association studies in an era of too much information - clinical analysis of new biomarker andgenetic data. Circulation, 116(17):1866-1870, 2007.
12. J. Loscalzo, I. Kohane, and A.-L. Barabasi. Human disease classification in the postgenomic era. Molecular Systems Biology, 2007.
13. J. B. Mitchell, T. Bubolz, J. E. Paul, C. I. Pashos, J. J. Escarce, L. H. Muhlbaier, J. M. Wiesman, W. W. Young, R. S. Epstein, and J. C. Javitt. Using medicare claims for outcomes research. Medical Care, 32:38-51, 1994.
14. F. Piscaglia, A. Cucchetti, A. Orlandini, E. Sagrini, A. Gianstefani, C. Crespi, G. Pelosi, M. Valli, L. Sacchelli, C. Ferrari, and L. Bolondi. Prediction of significant fibrosis in chronic hepatitis c patients by artificial neural network analysis of clinical factors, volume 39, March 2007.
15. R. Snyderman and R. S. Williams. Prospective medicine: The next health care transformation. Future Medicine, 2003.
16. D. T. Wong and W. A. Knaus. Predicting outcome in critical care: the current status of the apache prognostic scoring system. Canadian Journal of Anesthesia, 38:374-383, 1991.