Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care.
Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care.
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DOI：
10.1186/s12911-021-01712-6
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发表时间：
2021-12-17
影响因子：
3.5
通讯作者：
Zhong J
中科院分区：
文献类型：
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作者：
Zheng H;Zhu J;Xie W;Zhong J
患有严重冠状病毒病19(新冠肺炎)的患者通常需要补充氧气作为基本治疗。我们开发了一种基于深度强化学习(RL)的机器学习算法，用于重症监护下危重患者氧流量的持续管理，该算法可以识别出具有较强潜力的最佳个性化氧流量，从而相对于当前的临床实践来降低死亡率。我们将新冠肺炎患者的氧流轨迹和他们的健康结局建模为马尔可夫决策过程。根据患者个体特征和健康状况，利用深度确定性策略梯度(DDPG)学习最优氧气控制策略，并实时推荐氧流量以降低死亡率。我们通过使用来自纽约大学朗格尼健康门诊护理的1,372名具有电子健康记录的新冠肺炎危重患者的回顾队列，通过交叉验证来评估所提出方法的性能。RL算法下的平均死亡率低于护理标准下的2.57%(95%CI：2.08-3.06)(P <； 0.001)，从护理标准下的7.94%下降到我们提出的算法下的5.37%。平均推荐氧流量为1.28g L/分钟(95%可信区间：1.14~1.42)。因此，RL算法可能会带来更好的重症监护治疗，可以降低死亡率，同时节省稀缺的氧气资源。在新冠肺炎大流行期间，它可以减少氧气短缺问题，改善公共健康。新冠肺炎重症监护患者的个性化强化学习氧流量控制算法显示，与标准护理相比，7天死亡率显著降低。在与训练数据无关的总体交叉验证队列中，强化者的实际流速与RL决定相匹配的患者的死亡率最低。网上版载有补充材料，可在10.1186/s12911-021-01712-6查阅。
Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. We modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021. The mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08–3.06) reduction (P < 0.001) from 7.94% under the standard of care to 5.37% under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14–1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic. A personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists’ actual flow rate matched the RL decisions. The online version contains supplementary material available at 10.1186/s12911-021-01712-6.
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影响因子：
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作者：
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通讯作者：
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