机器学习对临床预测准确性和决策支持的死亡风险预测的影响:一个随机 Vignette 研究
Ravi B Parikh1,2,3,4, William J Ferrell2,3, Anthony Girard2
1Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
概括
机器学习 (ML) 提高了20.9%的晚期癌症临床医生的预后准确度. 然而,ML预测并没有改变有关息治疗或预先护理规划转诊的决定.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 机器学习 (ML) 算法显示出改善癌症等严重疾病的预后准确性的潜力.
- 确定可以从早期息治疗 (PC) 或预先护理计划 (ACP) 中受益的患者至关重要.
- ML介绍策略对临床医生的决策的影响需要进一步研究.
研究的目的:
- 评估假设ML算法的不同呈现策略如何影响临床医生的预后准确性.
- 评估ML预测对临床医生关于息护理和事先护理规划的决定的影响.
- 确定ML预后估计的最佳陈述策略.
主要方法:
- 在治疗转移性非小细胞肺癌 (mNSCLC) 的医疗瘤学家中进行了一项随机临床图片调查研究.
- 临床医生审查了不同预后风险和预计寿命的患者简历,推PC和ACP.
- 然后向临床医生展示了相同的图片,其中包含假设的ML存活率估计,按绝对和/或参考依赖的呈现随机排序.
主要成果:
- 通过ML呈现,预后准确度显著提高20.9% (P < 0.001).
- 绝对风险呈现策略,与或没有参考依赖,产生了更大的准确性收益.
- ML表现没有显著改变建议ACP (1.3%变化) 或PC转诊 (0.7%变化) 的比例.
结论:
- 基于ML的预后评估可以提高临床医生的预后准确性.
- 目前的ML表达策略似乎不会改变关于PC或ACP转诊的临床决策.
- 未来的ML算法应该优先考虑可解释性和绝对预测,以便对临床决策产生更大的影响.
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
8.3K
06:19Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
738
相关概念视频
Cancer Survival Analysis
457
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...
457
Kaplan-Meier Approach
276
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,...
276
Actuarial Approach
138
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,...
138
Comparing the Survival Analysis of Two or More Groups
297
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...
297
Steps in Outbreak Investigation
209
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
209
Issues And Trends In Healthcare Delivery System
5.9K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.9K
