Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1374162
Hongshu Liu, Nabeel Seedat, Julia Ive
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引用次数: 0

Abstract

Introduction: Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision-making in healthcare contexts. This is especially true since many state-of-the-art systems are trained using the data which have been labeled automatically (self-supervised mode) and tend to overfit.

Methods: In this study, we investigate the quality of uncertainty estimates from a range of current state-of-the-art predictive models applied to the problem of observation detection in radiology reports. This problem remains understudied for Natural Language Processing in the healthcare domain.

Results: We demonstrate that Gaussian Processes (GPs) provide superior performance in quantifying the risks of three uncertainty labels based on the negative log predictive probability (NLPP) evaluation metric and mean maximum predicted confidence levels (MMPCL), whilst retaining strong predictive performance.

Discussion: Our conclusions highlight the utility of probabilistic models applied to "noisy" labels and that similar methods could provide utility for Natural Language Processing (NLP) based automated labeling tasks.

为临床自然语言处理中的半监督学习自动数据标注中的分歧建模。
导言:提供不确定性准确估计值的计算模型对于医疗决策相关的风险管理至关重要。由于许多最先进的系统都是使用自动标注的数据(自我监督模式)进行训练的,因此往往会出现过拟合的情况,这一点尤为重要:在本研究中,我们将一系列当前最先进的预测模型应用于放射学报告中的观察结果检测问题,对其不确定性估计的质量进行了调查。这一问题在医疗保健领域的自然语言处理中仍未得到充分研究:结果:我们证明了高斯过程(GPs)在量化基于负对数预测概率(NLPP)评估指标和平均最大预测置信水平(MMPCL)的三种不确定性标签的风险方面具有卓越的性能,同时保持了强大的预测性能:我们的结论强调了应用于 "噪声 "标签的概率模型的实用性,类似的方法可为基于自然语言处理(NLP)的自动标签任务提供实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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