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首页> 外文期刊>BMC Medical Informatics and Decision Making >Combination of conditional random field with a rule based method in the extraction of PICO elements
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Combination of conditional random field with a rule based method in the extraction of PICO elements

机译：在PICO元素提取中将条件随机场与基于规则的方法相结合

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摘要

Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics is complex to capture it from unstructured information. The combination of the machine learning methods (MLMs) with rule based methods (RBMs) could facilitate and improve the PICO extraction. This paper studies the PICO elements extraction methods. The goal is to combine the MLMs with the RBMs to extract PICO elements in medical papers to facilitate answering clinical questions formulated with the PICO framework. First, we analyze the aspects of the MLM model that influence the quality of the PICO elements extraction. Secondly, we combine the MLM approach with the RBMs in order to improve the PICO elements retrieval process. To conduct our experiments, we use a corpus of 1000 abstracts. We obtain an F-score of 80% for P element, 64% for the I element and 92% for the O element. Given the nature of the used training corpus where P and I elements represent respectively only 6.5 and 5.8% of total sentences, the results are competitive with previously published ones. Our study of the PICO element extraction shows that the task is very challenging. The MLMs tend to have an acceptable precision rate but they have a low recall rate when the corpus is not representative. The RBMs backed up the MLMs to increase the recall rate and consequently the combination of the two methods gave better results.
机译：随着医疗信息量的增加以及从非结构化信息中获取信息的复杂性，健康信息的获取就很难从患者/问题，干预，比较和结果等方面提取被称为PICO要素的主要护理信息。机器学习方法（MLM）与基于规则的方法（RBM）的结合可以促进并改善PICO的提取。本文研究了PICO元素的提取方法。目标是将MLM与RBM结合起来以提取医学论文中的PICO元素，以帮助回答使用PICO框架提出的临床问题。首先，我们分析了MLM模型的各个方面，这些方面会影响PICO元素提取的质量。其次，我们将MLM方法与RBM结合起来，以改进PICO元素的检索过程。为了进行实验，我们使用了1000个摘要的语料库。我们获得P元素80％，I元素64％和O元素92％的F得分。考虑到所使用的训练语料库的性质，其中P和I元素分别仅占总句子的6.5和5.8％，结果与以前发布的相比。我们对PICO元素提取的研究表明，这项任务非常具有挑战性。 MLM倾向于具有可接受的准确率，但是当语料库不具有代表性时，其召回率很低。 RBM支持MLM，以提高召回率，因此，两种方法的组合可提供更好的结果。

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