用电子病历数据估计最佳治疗方案,使用剩余生命价值估计器
Grace Rhodes1, Marie Davidian2, Wenbin Lu2
1Eli Lilly and Company, Indianapolis, IN 46204, USA.
Biostatistics (Oxford, England)
|February 9, 2024
概括
这项研究介绍了ReLiVE-Q,这是一种优化动态治疗方案以最大限度地延长患者剩余寿命的新方法. ReLiVE-Q使用电子医疗记录数据来个性化治疗决策,如败血症等疾病.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 关键护理医学 关键护理医学
背景情况:
- 动态治疗方案对于管理慢性和危及生命的疾病至关重要.
- 电子医疗记录 (EMR) 数据为顺序治疗决策提供了丰富的信息.
- 优化治疗以最大限度地延长剩余寿命是重症监护的关键目标,特别是对于诸如败血症等疾病.
研究的目的:
- 引入剩余寿命值估计器 (ReLiVE) 用于估计固定处理方案下的预期剩余寿命.
- 开发和介绍ReLiVE-Q,这是一种估计最佳动态处理方案的方法,可以最大限度地提高预期的累积限制剩余寿命.
- 证明ReLiVE-Q在模拟和现实世界EMR数据中的实用性,以实现个性化治疗优化.
主要方法:
- 开发剩余生命价值估计器 (ReLiVE).
- 在ReLiVE-Q框架内应用逆向诱导算法Q-learning.
- 使用模拟研究和分析来自多参数智能监控重症监护 (MIMIC) 数据库的EMR数据的验证.
主要成果:
- ReLiVE-Q有效地估计了预期的累积限制剩余寿命.
- 该方法成功地在模拟场景中确定了最佳的动态处理方案.
- 对败血症患者数据的应用表明ReLiVE-Q能够个性化治疗以改善治疗结果.
结论:
- ReLiVE-Q提供了一种可靠的方法来估计最佳的动态治疗方案.
- 该方法利用EMR数据来个性化治疗决策,旨在最大限度地提高剩余寿命.
- 这种方法具有显著的潜力,可以改善像败血症这样的关键病症患者的护理.
关键词:
这就是MIMIC-III.这就是Q-learning.背景向量是一个上下文向量.动态处理制度 动态处理制度电子医疗记录 电子医疗记录精准医学是一门精准医学.随机的森林随机的森林剩余寿命 剩余寿命这是一种血症.更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
09:00TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
Published on: April 13, 2021
4.5K
相关概念视频
Kaplan-Meier Approach
138
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
138
Actuarial Approach
78
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
78
Methods of Documentation VII: EMR
835
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
835
Cancer Survival Analysis
346
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
346
Comparing the Survival Analysis of Two or More Groups
186
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
186
Introduction To Survival Analysis
237
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
237
