Robust Policies For Proactive ICU Transfers
Robust Policies For Proactive ICU Transfers
复制标题
积极主动 ICU 转移的强有力政策
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
G. Escobar
中科院分区：
文献类型：
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作者：
Julien Grand;Carri W. Chan;Vineet Goyal;G. Escobar
计划外转入重症监护室（ICU）的患者的死亡率往往高于直接进入ICU的患者。机器学习在预测患者病情恶化方面的最新进展已经引入了从病房到ICU的主动转移的可能性。在这项工作中，我们研究的问题，找到\n {鲁棒}的病人转移政策，占统计估计的不确定性，由于数据的限制，优化时，以改善整体病人护理。我们提出了一个马尔可夫决策过程模型来捕捉病人健康的演变，其中的状态代表了病人的严重程度。在相当一般的假设下，我们证明了最优转移策略具有阈值结构，即，它将所有超过一定严重程度的患者转移到ICU（取决于可用容量）。由于模型参数通常是基于来自真实世界数据的统计估计来确定的，因此它们固有地受到错误指定和估计误差的影响。我们考虑到这个参数的不确定性，推导出一个强大的政策，优化最坏情况下的奖励在所有合理的值的模型参数。我们证明了在相当一般的假设下，鲁棒策略也具有阈值结构。此外，它是更积极的转移病人比最优的名义政策，其中没有考虑参数的不确定性。我们目前的计算实验使用的数据集在21 KNPC医院的住院治疗，并提出经验证据的敏感性，各种医院指标（死亡率，住院时间，平均ICU占用率）的参数的微小变化。我们的工作提供了有用的见解参数的不确定性的影响，得出简单的政策，积极的ICU转移，有很强的经验性能和理论保证。
Patients whose transfer to the Intensive Care Unit (ICU) is unplanned are prone to higher mortality rates than those who were admitted directly to the ICU. Recent advances in machine learning to predict patient deterioration have introduced the possibility of \emph{proactive transfer} from the ward to the ICU. In this work, we study the problem of finding \emph{robust} patient transfer policies which account for uncertainty in statistical estimates due to data limitations when optimizing to improve overall patient care. We propose a Markov Decision Process model to capture the evolution of patient health, where the states represent a measure of patient severity. Under fairly general assumptions, we show that an optimal transfer policy has a threshold structure, i.e., that it transfers all patients above a certain severity level to the ICU (subject to available capacity). As model parameters are typically determined based on statistical estimations from real-world data, they are inherently subject to misspecification and estimation errors. We account for this parameter uncertainty by deriving a robust policy that optimizes the worst-case reward across all plausible values of the model parameters. We show that the robust policy also has a threshold structure under fairly general assumptions. Moreover, it is more aggressive in transferring patients than the optimal nominal policy, which does not take into account parameter uncertainty. We present computational experiments using a dataset of hospitalizations at 21 KNPC hospitals, and present empirical evidence of the sensitivity of various hospital metrics (mortality, length-of-stay, average ICU occupancy) to small changes in the parameters. Our work provides useful insights into the impact of parameter uncertainty on deriving simple policies for proactive ICU transfer that have strong empirical performance and theoretical guarantees.