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 sub-population 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.
|Original language||English (US)|
|Number of pages||12|
|Journal||Pacific Symposium on Biocomputing|
|State||Published - 2018|
|Event||23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States|
Duration: Jan 3 2018 → Jan 7 2018
Bibliographical noteFunding Information:
We thank NIH-1R01LM011566-01 to support this work.
© 2017 The Authors.
- Clinical trial simulation
- Personalized treatment
- Precision medicine