Hypotension in critical care settings is a life-threatening emergency that
must be recognized and treated early. While fluid bolus therapy and
vasopressors are common treatments, it is often unclear which interventions to
give, in what amounts, and for how long. Observational data in the form of
electronic health records can provide a source for helping inform these choices
from past events, but often it is not possible to identify a single best
strategy from observational data alone. In such situations, we argue it is
important to expose the collection of plausible options to a provider. To this
end, we develop SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement
Learning, to identify distinct treatment options that are supported in the
data. We demonstrate SODA-RL on a cohort of 10,142 ICU stays where hypotension
presented. Our learned policies perform comparably to the observed physician
behaviors, while providing different, plausible alternatives for treatment
decisions.

通过采用 SODA-RL (Safely Optimized, Diverse, and Accurate Reinforcement Learning) 方法，从 10,142 个重症监护室住院患者的医疗记录中分析和确认了多种有效的治疗选择，这可以作为医生决策的参考，提高危重症监护室治疗水平。

使用 SODA-RL 识别急性低血压的独特有效治疗方法：安全优化多元准确强化学习

Identifying Distinct, Effective Treatments for Acute Hypotension with  SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning

Critically ill patients in regular wards are vulnerable to unanticipated
adverse events which require prompt transfer to the intensive care unit (ICU).
To allow for accurate prognosis of deteriorating patients, we develop a novel
continuous-time probabilistic model for a monitored patient's temporal sequence
of physiological data. Our model captures "informatively sampled" patient
episodes: the clinicians' decisions on when to observe a hospitalized patient's
vital signs and lab tests over time are represented by a marked Hawkes process,
with intensity parameters that are modulated by the patient's latent clinical
states, and with observable physiological data (mark process) modeled as a
switching multi-task Gaussian process. In addition, our model captures
"informatively censored" patient episodes by representing the patient's latent
clinical states as an absorbing semi-Markov jump process. The model parameters
are learned from offline patient episodes in the electronic health records via
an EM-based algorithm. Experiments conducted on a cohort of patients admitted
to a major medical center over a 3-year period show that risk prognosis based
on our model significantly outperforms the currently deployed medical risk
scores and other baseline machine learning algorithms.

该研究发展了一个连续时间概率模型以用于监测危重患者的生理数据，并对这些患者进行风险预测，该模型的预测结果优于目前的医疗风险评分和其他基线机器学习算法。

从临床判断中学习：基于半马尔可夫调制带标记霍克斯过程的风险预测

Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes  Processes for Risk Prognosis

Objective: In this paper, we develop a personalized real-time risk scoring
algorithm that provides timely and granular assessments for the clinical acuity
of ward patients based on their (temporal) lab tests and vital signs; the
proposed risk scoring system ensures timely intensive care unit (ICU)
admissions for clinically deteriorating patients. Methods: The risk scoring
system learns a set of latent patient subtypes from the offline electronic
health record data, and trains a mixture of Gaussian Process (GP) experts,
where each expert models the physiological data streams associated with a
specific patient subtype. Transfer learning techniques are used to learn the
relationship between a patient's latent subtype and her static admission
information (e.g. age, gender, transfer status, ICD-9 codes, etc). Results:
Experiments conducted on data from a heterogeneous cohort of 6,321 patients
admitted to Ronald Reagan UCLA medical center show that our risk score
significantly and consistently outperforms the currently deployed risk scores,
such as the Rothman index, MEWS, APACHE and SOFA scores, in terms of
timeliness, true positive rate (TPR), and positive predictive value (PPV).
Conclusion: Our results reflect the importance of adopting the concepts of
personalized medicine in critical care settings; significant accuracy and
timeliness gains can be achieved by accounting for the patients' heterogeneity.
Significance: The proposed risk scoring methodology can confer huge clinical
and social benefits on more than 200,000 critically ill inpatient who exhibit
cardiac arrests in the US every year.

在这篇论文中，我们提出了一种个性化的实时风险评分算法，基于病人的（时间性）实验室测试和生命体征提供及时和细致的临床状况评估，确保临床恶化的病人能够及时转入重症监护室，该风险评分系统通过从离线电子健康记录数据中学习一组潜在的患者亚型，并训练一组高斯过程 (GP) 专家，每个专家可以模拟与特定患者亚型相关的生理数据流，从而实现。