Using simulation and optimization approach to improve outcome through warfarin precision treatment

Chih Lin Chi, Lu He, Kourosh Ravvaz, John Weissert, Peter J. Tonellato

Research output: Contribution to journalConference articlepeer-review

Abstract

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 languageEnglish (US)
Pages (from-to)412-423
Number of pages12
JournalPacific Symposium on Biocomputing
Volume0
Issue number212669
DOIs
StatePublished - 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018

Bibliographical note

Funding Information:
We thank NIH-1R01LM011566-01 to support this work.

Publisher Copyright:
© 2017 The Authors.

Keywords

  • Clinical trial simulation
  • Optimization
  • Personalized treatment
  • Precision medicine

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