文献选工具的开发和验证:系统审查中的少量学习方法
Phongphat Wiwatthanasetthakarn1, Wanchana Ponthongmak1, Panu Looareesuwan1
1Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Journal of medical Internet research
|December 11, 2024
概括
本研究引入了使用S-BERT简化系统审查 (SR) 选的几次学习 (FSL) 框架,减少了50%以上的工作量,但有可能错过符合条件的研究.
科学领域:
- 机器学习应用 机器学习应用
- 基于证据的医学基于证据的医学.
- 生物医学信息学 生物医学信息学
背景情况:
- 系统性审查 (SRs) 对基于证据的医学至关重要,但由于时间密集的文献选,它们面临着挑战.
- 快速的医学进步可以很快使SR变得过时,需要有效的查方法.
- 靠近的学习 (FSL) 和句子-BERT (S-BERT) 提供了有前途的解决方案,以简化SR研究选择有限的数据.
研究的目的:
- 开发和验证一个模型框架,利用FSL在SR中进行高效的研究选.
- 目标是减少与文献选相关的工作量,同时保持高的召回率.
- 评估FSL在加速SR过程中的可行性和性能.
主要方法:
- 使用S-BERT开发并验证了一个新的FSL模型框架,其中包含了9个之前的SR项目的标题和摘要.
- 关键指标包括工作量减少和等号相似性被用来确定最佳的训练数据大小 (4-12项研究).
- 在4个正在进行的SR中,前性评估将FSL选与二次审查员和黄金标准主要审查员进行了比较,以估计虚假负面率.
主要成果:
- 该FSL模型实现了显著的工作量减少 (51.11%至97.67%) 的最佳培训组 4-6 符合条件的研究.
- 100%召回的相似度值在0.432到0.636.6之间.
- 在前性评估中,FSL模型的虚假负面率 (1.87%-12.20%) 与主要审稿人相比,通常低于二次审稿人的 (5%-56.48%).
结论:
- 开发的FSL框架显示了减少系统审查选工作量50%以上的巨大潜力.
- 该模型实现了大量的工作量减少,但不能保证100%的召回,表明存在遗漏相关研究的风险.
- 未来的开发应该集中在创建一个Web应用程序,以提高FSL框架的可访问性和实施性.
相关概念视频
Proofreading
Synthesis of new DNA molecules starts when DNA polymerase links nucleotides together in a sequence that is complementary to the template DNA strand. DNA polymerase has a higher affinity for the correct base to ensure fidelity in DNA replication. The DNA polymerase furthermore proofreads during replication, using an exonuclease domain that cuts off incorrect nucleotides from the nascent DNA strand.Errors during Replication Are Corrected by the DNA Polymerase EnzymeGenomic DNA is synthesized in...
Types of Errors: Detection and Minimization
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Quantifying and Rejecting Outliers: The Grubbs Test
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Development of Analytical Methods
An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
Data Validation
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:


