Abstract
This study introduces a novel game-theoretic methodology to enhancing the optimization of Anti-Epileptic Drug (AED) selection for patients diagnosed with Juvenile Myoclonic Epilepsy (JME). The primary motivation is the need to enhance the treatment of JME by leveraging game theory principles to make informed decisions about AEDs. Within this framework, a novel cooperative Nash-based game model is proposed within the medical domain. It focuses on patients utilizing two distinct types of ADEs: Valproate (VPA) and Lamotrigine (LTG) to manage seizures while minimizing overall medical and non-medical expenses. The innovation lies in formulating a dynamic decision-making framework that integrates the patient’s medical treatment selection, offering a unique Nash equilibrium decision under certain conditions. This work presents a patient-centered decision-making model to balance the tradeoff between cost-based and drug-based contributions in epilepsy management. By linking medical decision-making through the equilibrium strategy, the study paves the way for personalized epilepsy treatment in JME management. Our model’s application demonstrates that, under the equilibrium condition, the probability of selecting either VPA or LTG is 50%, indicating an equal preference for both drugs in managing JME. Sensitivity analyses on VPA and LTG parameters confirm the stability and reliability of this Nash equilibrium decision, reinforcing the model’s applicability in clinical decision-making as well as assessing the stability and reliability of the Nash equilibrium decision in the treatment of JME.
Original language | English |
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Pages (from-to) | 3596-3616 |
Number of pages | 21 |
Journal | Journal of Industrial and Management Optimization |
Volume | 20 |
Issue number | 12 |
DOIs | |
Publication status | Published - Dec 2024 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© (2024), (American Institute of Mathematical Sciences). All rights reserved.
Keywords
- epilepsy
- equilibrium
- Game theory
- healthcare
- management
- optimization