Chih-Lin Chi1, Lu He2, Kourosh Ravvaz3, John Weissert3, Peter J. Tonellato4,5
1School of Nursing & Institute for Health Informatics, University of Minnesota
2Computer Science and Engineering, University of Minnesota
3Aurora Health Care
4Department of Biomedical Informatics, Department of Pathology, Harvard Medical School
5Zilber School of Public Health, University of Wisconsin-Milwaukee
Email: cchi@umn.edu, Peter_Tonellato@hms.harvard.edu
Pacific Symposium on Biocomputing 23:412-423(2018)
© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
We apply a treatment simulation and optimization approach to develop decision support guidance for warfarin precision treatment plans. Simulation include the use of ~1,500,000 clinical avatars (simulated patients) generated by an integrated data-driven and domain-knowledge based Bayesian Network Modeling approach. Subsequently, we simulate 30-day individual patient response to warfarin treatment of five clinical and genetic treatment plans followed by both individual and subpopulation based optimization. Sub-population optimization (compared to individual optimization) provides a cost effective and realistic means of implementation of a precision-driven treatment plan in practical settings. In this project, we use the property of minimal entropy to minimize overall adverse risks for the largest possible patient sub-populations and we temper the results by considering both transparency and ease of implementation. Finally, we discuss the improved outcome of the precision treatment plan based on the sub-population optimized decision support rules.