TY - GEN
T1 - Online transfer learning for differential diagnosis determination
AU - Xu, Jie
AU - Sow, Daby
AU - Turaga, Deepak
AU - Van Der Schaar, Mihaela
N1 - Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - In this paper we present a novel online transfer learning approach to determine the set of tests to perform, and the sequence in which they need to be performed, in order to develop an accurate diagnosis while minimizing the cost of performing the tests. Our learning approach can be incorporated as part of a clinical decision support system (CDSS) with which clinicians can interact. The approach builds on a contextual bandit framework and uses online transfer learning to overcome limitations with the availability of rich training data sets that capture different conditions, context, test results as well as outcomes. We provide confidence bounds for our recommended policies, which is essential in order to build the trust of clinicians. We evaluate the algorithm against different transfer learning approaches on real-world patient alarm datasets collected from Neurological Intensive Care Units (with reduced costs by 20%).
AB - In this paper we present a novel online transfer learning approach to determine the set of tests to perform, and the sequence in which they need to be performed, in order to develop an accurate diagnosis while minimizing the cost of performing the tests. Our learning approach can be incorporated as part of a clinical decision support system (CDSS) with which clinicians can interact. The approach builds on a contextual bandit framework and uses online transfer learning to overcome limitations with the availability of rich training data sets that capture different conditions, context, test results as well as outcomes. We provide confidence bounds for our recommended policies, which is essential in order to build the trust of clinicians. We evaluate the algorithm against different transfer learning approaches on real-world patient alarm datasets collected from Neurological Intensive Care Units (with reduced costs by 20%).
UR - http://www.scopus.com/inward/record.url?scp=84964615091&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84964615091
T3 - AAAI Workshop - Technical Report
SP - 25
EP - 29
BT - The World Wide Web and Public Health Intelligence - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report
PB - AI Access Foundation
T2 - 29th AAAI Conference on Artificial Intelligence, AAAI 2015
Y2 - 25 January 2015 through 30 January 2015
ER -