慢性病的数据驱动建模与预测控制：溃疡性结肠炎应用

中文摘要 AI 辅助

慢性病的临床管理天然地构成一个反馈控制问题。医生在门诊时观察疾病状态，并根据患者病史和当前状态做出决策。我们提出一个框架，将慢性病对治疗反应的动态建模为基于事件的受控随机系统，其中治疗决策在离散、不规则间隔的事件中做出，而疾病在事件之间作为连续时间马尔可夫链（CTMC）演化，其转移率依赖于状态和输入。我们在溃疡性结肠炎背景下展示该框架，从电子健康记录（EHR）中超过3000名溃疡性结肠炎患者的真实临床数据中识别模型。利用学习到的模型，我们将治疗选择表述为模型预测控制（MPC）问题，该问题最大化处于非活动疾病状态的预期时间，并评估所得策略。在10名患者的闭环模拟中，MPC在10名患者中的4名的大多数实现中达到等于或低于观察到的临床过程的成本，且在所有情况下的结果均与观察到的护理相当，并符合临床指南。

英文摘要

The clinical management of chronic disease is naturally structured as a feedback control problem. Physicians observe the disease state at clinic visits and make decisions based on the patient's history and current state. We propose a framework that models the dynamics of chronic disease in response to therapy as an event-based controlled stochastic system, in which treatment decisions are made at discrete, irregularly spaced events while the disease evolves between events as a continuous-time Markov chain (CTMC) with state and input dependent transition rates. We demonstrate this framework in the setting of ulcerative colitis, where we identify the model from real-world clinical data in the electronic health record (EHR) of more than 3,000 patients with ulcerative colitis. Using the learned model, we formulate therapy selection as a model predictive control problem (MPC) that maximizes the expected time spent in inactive disease states, and we evaluate the resulting policy. In closed-loop simulation on 10 patients, the MPC achieves a cost at or below the observed clinical course in the majority of its realizations for 4 of the 10 patients, and its outcomes in all cases are comparable to observed care and within clinical guidelines.

发表机构

• University of California, Berkeley（加州大学伯克利分校）
• Bakar Computational Health Sciences Institute, University of California, San Francisco（加州大学旧金山分校巴克尔计算健康科学研究所）

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