Hepatitis B Virus Infection Control Using Reinforcement Learning
عنوان مقاله: Hepatitis B Virus Infection Control Using Reinforcement Learning
شناسه ملی مقاله: ICEEE03_006
منتشر شده در سومین کنفرانس مهندسی برق و الکترونیک ایران در سال 1390
شناسه ملی مقاله: ICEEE03_006
منتشر شده در سومین کنفرانس مهندسی برق و الکترونیک ایران در سال 1390
مشخصات نویسندگان مقاله:
Amin Noori - Department of Control Engineering Ferdowsi university of MashhadMashhad, Iran
M.B Naghibi-sistani
خلاصه مقاله:
Amin Noori - Department of Control Engineering Ferdowsi university of MashhadMashhad, Iran
M.B Naghibi-sistani
In this paper, optimal drug schedule for patients infected by hepatitis B virus (HBV) is obtained. An objective of the control is reducing infected cells and free virions. The optimal control problem is to design an effective drug-schedule to reduce the number of infected cells and free virions in a time-optimal fashion. To achieve this goal, a reinforcement learning (RL), which is one of the best unsupervised machine learning algorithms, is proposed for control. Because RL has no need of environment model, i.e. it is model-free; it has absorbed interests during the recent years, especially in medical applications. Performance evaluation of the proposed algorithm has been performed by simulating on the mathematical model of drug dosage of hepatitis therapy.
کلمات کلیدی: drug therapy; hepatitis B virus infection; optimal control; reinforcement learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/125241/