TY - GEN
T1 - Predicting individual disease risk based on medical history
AU - Davis, Darcy A.
AU - Chawla, Nitesh V.
AU - Blumm, Nicholas
AU - Barabási, Albert László
AU - Christakis, Nicholas
PY - 2008
Y1 - 2008
N2 - The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost effcient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on a patient's medical history using ICD- 9-CM codes in order to predict future diseases risks. CARE uses collaborative filltering to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.
AB - The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost effcient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on a patient's medical history using ICD- 9-CM codes in order to predict future diseases risks. CARE uses collaborative filltering to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.
KW - Collaborative filltering
KW - Disease risk prediction
KW - Ensemble
KW - Pospective health care
UR - http://www.scopus.com/inward/record.url?scp=70349260874&partnerID=8YFLogxK
U2 - 10.1145/1458082.1458185
DO - 10.1145/1458082.1458185
M3 - Conference contribution
AN - SCOPUS:70349260874
SN - 9781595939913
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 769
EP - 778
BT - Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08
T2 - 17th ACM Conference on Information and Knowledge Management, CIKM'08
Y2 - 26 October 2008 through 30 October 2008
ER -