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Control-theoretic scheduling of psychotherapy and pharmacotherapy for the treatment of post-traumatic stress disorder

机译:心理和药物治疗创伤后应激障碍的控制理论安排

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摘要

Control technology has found widespread use in medical applications, including pharmacology and drug dosing. Although mental and behavioural health applications have much in common with these systems, only a few seminal works have applied control technology to inform treatment with non-pharmacological interventions. Herein, a model-based, predictive control strategy is proposed to optimise treatment for post-traumatic stress disorder (PTSD). A mathematical model is developed that captures the dynamic relationship between five PTSD treatments and three commonly-employed PTSD assessment scales. The model supports an adaptive model predictive control strategy in which treatments are scheduled to achieve a therapeutic objective while recurrent assessment scale data are used to adaptively identify the patient model parameters. The model is corroborated against an independent data set, and the efficacy of the adaptive control strategy is demonstrated through Monte-Carlo simulations. While this work represents only a preliminary step towards a quantitative tool for clinical use, the simulated treatment results indicate that the proposed model and control approach could provide valuable insight towards designing personalised intervention schedules that increase treatment adherence, lower treatment costs and provide PTSD patients with more effective care.
机译:控制技术已在医学应用中得到广泛使用,包括药理学和药物剂量。尽管心理和行为健康应用程序与这些系统有很多共同点,但只有少数开创性的工作应用了控制技术来通过非药物干预为治疗提供信息。本文中,提出了一种基于模型的预测控制策略,以优化创伤后应激障碍(PTSD)的治疗。建立了一个数学模型,该模型捕获了五种PTSD治疗与三种常用PTSD评估量表之间的动态关系。该模型支持自适应模型预测控制策略,在该策略中,安排治疗计划以达到治疗目的,同时使用复发评估量表数据自适应地识别患者模型参数。该模型针对独立的数据集得到了证实,并且自适应控制策略的有效性通过蒙特卡洛仿真得到了证明。虽然这项工作只是迈向临床使用定量工具的第一步,但是模拟的治疗结果表明,所提出的模型和控制方法可以为设计个性化干预计划提供有价值的见识,从而增加治疗依从性,降低治疗成本并为PTSD患者提供更有效的护理。

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