TY - GEN
T1 - Robust local explanations for healthcare predictive analytics
T2 - 40th International Conference on Information Systems, ICIS 2019
AU - Kim, Buomsoo
AU - Srinivasan, Karthik
AU - Ram, Sudha
N1 - Publisher Copyright:
© 40th International Conference on Information Systems, ICIS 2019. All rights reserved.
PY - 2019
Y1 - 2019
N2 - With recent advancements in data analytics, healthcare predictive analytics (HPA) is garnering growing interest among practitioners and researchers. However, it is risky to blindly accept the results and users will not accept the HPA model if transparency is not guaranteed. To address this challenge, we propose the RObust Local EXplanations (ROLEX) method, which provides robust, instance-level explanations for any HPA model. The applicability of the ROLEX method is demonstrated using the fragility fracture prediction problem. Analysis with a large real-world dataset demonstrates that our method outperforms state-of-the-art methods in terms of local fidelity. The ROLEX method is applicable to various types of HPA problems beyond the fragility fracture problem. It is applicable to any type of supervised learning model and provides fine-grained explanations that can improve understanding of the phenomenon of interest. Finally, we discuss theoretical implications of our study in light of healthcare IS, big data, and design science.
AB - With recent advancements in data analytics, healthcare predictive analytics (HPA) is garnering growing interest among practitioners and researchers. However, it is risky to blindly accept the results and users will not accept the HPA model if transparency is not guaranteed. To address this challenge, we propose the RObust Local EXplanations (ROLEX) method, which provides robust, instance-level explanations for any HPA model. The applicability of the ROLEX method is demonstrated using the fragility fracture prediction problem. Analysis with a large real-world dataset demonstrates that our method outperforms state-of-the-art methods in terms of local fidelity. The ROLEX method is applicable to various types of HPA problems beyond the fragility fracture problem. It is applicable to any type of supervised learning model and provides fine-grained explanations that can improve understanding of the phenomenon of interest. Finally, we discuss theoretical implications of our study in light of healthcare IS, big data, and design science.
KW - Explainable artificial intelligence
KW - Fragility fracture
KW - Healthcare predictive analytics
UR - http://www.scopus.com/inward/record.url?scp=85114902619&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85114902619
T3 - 40th International Conference on Information Systems, ICIS 2019
BT - 40th International Conference on Information Systems, ICIS 2019
PB - Association for Information Systems
Y2 - 15 December 2019 through 18 December 2019
ER -