TY - GEN
T1 - Towards intelligent decision making for risk screening
AU - Moutafis, Panagiotis
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
Copyright 2014 ACM.
PY - 2014/5/27
Y1 - 2014/5/27
N2 - Predicting the best next test for medical diagnosis is crucial as it can speed up diagnosis and reduce medical expenses. This determination should be made by fully utilizing the available information in a personalized manner for each patient. In this paper, we propose a method that uses synthesis to infer the best learning cohort for the patient under consideration. The constrained sample space is then used to select the best next test by maximizing the expected information gain.
AB - Predicting the best next test for medical diagnosis is crucial as it can speed up diagnosis and reduce medical expenses. This determination should be made by fully utilizing the available information in a personalized manner for each patient. In this paper, we propose a method that uses synthesis to infer the best learning cohort for the patient under consideration. The constrained sample space is then used to select the best next test by maximizing the expected information gain.
KW - Information gain
KW - Synthesis
UR - http://www.scopus.com/inward/record.url?scp=84939230622&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84939230622&partnerID=8YFLogxK
U2 - 10.1145/2674396.2674431
DO - 10.1145/2674396.2674431
M3 - Conference contribution
AN - SCOPUS:84939230622
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2014
PB - Association for Computing Machinery
T2 - 7th ACM International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2014
Y2 - 27 May 2014 through 30 May 2014
ER -