Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach
Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach
复制标题
DOI：
10.1016/j.artmed.2012.12.003
复制
发表时间：
2013-01-01
影响因子：
7.5
通讯作者：
Hauser, Kris
中科院分区：
文献类型：
--
作者：
Bennett, Casey C.;Hauser, Kris
目标：在现代医疗保健系统中，成本/复杂性迅速扩大，治疗方案不断增加，信息流呈爆炸式增长，而这些信息流往往无法有效到达前线，随着时间的推移，这些信息流阻碍了选择最佳治疗决策的能力。本文的目标是开发一个通用（非特定疾病）计算/人工智能（AI）框架来应对这些挑战。该框架具有两个潜在功能：（1）用于探索各种医疗保健政策、支付方法等的模拟环境，以及（2）临床人工智能的基础——可以“像医生一样思考”的人工智能。方法：这种方法结合了马尔可夫决策过程和动态决策网络，从临床数据中学习，并通过模拟替代顺序决策路径来制定复杂的计划，同时捕获医疗保健系统中各个组件有时相互冲突、有时协同的相互作用。它可以在部分可观察的环境中运行（在缺少观察或数据的情况下），通过维护有关患者健康状况的信念状态，并作为在线代理发挥作用，在执行行动和获得新观察时进行计划和重新计划。使用电子健康记录中的真实患者数据对该框架进行了评估。 结果：结果证明了该方法的可行性；这样的人工智能框架很容易优于当前的照常治疗（TAU）病例率/按服务付费的医疗保健模式。 AI 与 TAU 的每单位结果改变成本 (CPUC) 分别为 189 美元和 497 美元（较低的被认为是最佳），同时 AI 方法可以将患者的治疗结果提高 30-35%。调整某些人工智能模型参数可以进一步增强这一优势，以大约一半的成本获得大约 50% 的改进（结果变化）。 结论：经过仔细的设计和问题表述，即使在复杂和不确定的环境中，人工智能模拟框架也可以近似最佳决策。未来的工作概述了个性化医疗机器学习算法的潜在研究和集成方向。 (C) 2012 Elsevier B.V. 保留所有权利。
Objective: In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor".Methods: This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record.Results: The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs.Conclusion: Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. (C) 2012 Elsevier B.V. All rights reserved.