论文标题
建模在临床自然语言处理中的半监督学习的自动数据标签中建模分歧
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing
论文作者
论文摘要
提供准确估计其不确定性的计算模型对于与医疗保健环境中的决策相关的风险管理至关重要。尤其如此，因为许多最先进的系统都是使用已自动标记（自我监督模式）并倾向于过度标记的数据训练的。在这项工作中，我们研究了从放射学报告中应用于观察发现问题的一系列最新预测模型的不确定性估计质量。对于医疗保健领域中的自然语言处理，此问题仍在研究。我们证明，高斯工艺（GPS）在量化3个不确定性标签的风险方面具有卓越的性能，基于负对数预测概率（NLPP）评估度量指标和平均最大预测置信度（MMPCL），同时保留强大的预测性能。
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 has been labelled automatically (self-supervised mode) and tend to overfit. In this work, 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. We demonstrate that Gaussian Processes (GPs) provide superior performance in quantifying the risks of 3 uncertainty labels based on the negative log predictive probability (NLPP) evaluation metric and mean maximum predicted confidence levels (MMPCL), whilst retaining strong predictive performance.
