TY - GEN
T1 - Risk-O-Meter
T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
AU - Zolfaghar, Kiyana
AU - Agarwal, Jayshree
AU - Sistla, Deepthi
AU - Chin, Si Chi
AU - Roy, Senjuti Basu
AU - Verbiest, Nele
N1 - Publisher Copyright:
Copyright © 2013 ACM.
PY - 2013/8/11
Y1 - 2013/8/11
N2 - We present a system called Risk-O-Meter to predict and analyze clinical risk via data imputation, visualization, predictive modeling, and association rule exploration. Clinical risk calculators provide information about a person's chance of having a disease or encountering a clinical event. Such tools could be highly useful to educate patients to understand and monitor their health conditions. Unlike existing risk calculators that are primarily designed for domain experts, Risk- O-Meter is useful to patients who are unfamiliar with medical terminologies, or providers who have limited information about a patient. Risk-O-Meter is designed in a way such that it is exible enough to accept limited or incomplete data in- puts, and still manages to predict the clinical risk efficiently and effiectively. Current version of Risk-O-Meter evaluates 30-day risk of hospital readmission. However, the proposed system framework is applicable to general clinical risk pre- dictions. In this demonstration paper, we describe different components of Risk-O-Meter and the intelligent algorithms associated with each of these components to evaluate risk of readmission using incomplete patient data inputs.
AB - We present a system called Risk-O-Meter to predict and analyze clinical risk via data imputation, visualization, predictive modeling, and association rule exploration. Clinical risk calculators provide information about a person's chance of having a disease or encountering a clinical event. Such tools could be highly useful to educate patients to understand and monitor their health conditions. Unlike existing risk calculators that are primarily designed for domain experts, Risk- O-Meter is useful to patients who are unfamiliar with medical terminologies, or providers who have limited information about a patient. Risk-O-Meter is designed in a way such that it is exible enough to accept limited or incomplete data in- puts, and still manages to predict the clinical risk efficiently and effiectively. Current version of Risk-O-Meter evaluates 30-day risk of hospital readmission. However, the proposed system framework is applicable to general clinical risk pre- dictions. In this demonstration paper, we describe different components of Risk-O-Meter and the intelligent algorithms associated with each of these components to evaluate risk of readmission using incomplete patient data inputs.
KW - Association rule mining
KW - Clinical risk calculator
KW - Clustering
KW - Risk of hospital readmission predication
UR - http://www.scopus.com/inward/record.url?scp=85013035653&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013035653&partnerID=8YFLogxK
U2 - 10.1145/2487575.2487717
DO - 10.1145/2487575.2487717
M3 - Conference contribution
AN - SCOPUS:85013035653
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1518
EP - 1521
BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A2 - Parekh, Rajesh
A2 - He, Jingrui
A2 - Inderjit, Dhillon S.
A2 - Bradley, Paul
A2 - Koren, Yehuda
A2 - Ghani, Rayid
A2 - Senator, Ted E.
A2 - Grossman, Robert L.
A2 - Uthurusamy, Ramasamy
PB - Association for Computing Machinery
Y2 - 11 August 2013 through 14 August 2013
ER -