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首页> 外文期刊>Journal of stroke and cerebrovascular diseases: The official journal of National Stroke Association >Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke
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Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke

机译：对医疗摘要的自动评估：情绪分析的当前价值和机器学习对中风中随机试验的热量摘要的重要性

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Background: Text mining with automatic extraction of key features is gaining increasing importance in science and particularly medicine due to the rapidly increasing number of publications. Objectives: Here we evaluate the current potential of sentiment analysis and machine learning to extract the importance of the reported results and conclusions of randomized trials on stroke. Methods: PubMed abstracts of 200 recent reports of randomized trials were reviewed and manually classified according to the estimated importance of the studies. Importance of the papers was classified as "game changer", "suggestive", "maybe" "negative result". Algorithmic sentiment analysis was subsequently used on both the "Results" and the "Conclusions" paragraphs, resulting in a numerical output for polarity and subjectivity. The result of the human assessment was then compared to polarity and subjectivity. In addition, a neural network using the Keras platform built on Tensorflow and Python was trained to map the "Results" and "Conclusions" to the dichotomized human assessment (1: "game changer" or "suggestive"; 0:"maybe" or "negative", or no results reported). 120 abstracts were used as the training set and 80 as the test set. Results: 9 out of the 200 reports were classified manually as "game changer", 40 as "suggestive", 73 as "maybe" and 32 and "negative"; 46 abstracts did not contain any results. Polarity was generally higher for the "Conclusions" than for the "Results". Polarity was highest for the "Conclusions" classified as "suggestive". Subjectivity was also higher in the classes "suggestive" and "maybe" than in the classes "game changer" and "negative". The trained neural network provided a correct dichotomized output with an accuracy of 71% based on the "Results" and 73% based on "Conclusions". Conclusions: Current statistical approaches to text analysis can grasp the impact of scientific medical abstracts to a certain degree. Sentiment analysis showed that mediocre results are apparently written in more enthusiastic words than clearly positive or negative results. (c) 2020 Elsevier Inc. All rights reserved.
机译：背景：随着出版物数量的迅速增加，自动提取关键特征的文本挖掘在科学领域尤其是医学领域正变得越来越重要。目的：在这里，我们评估情绪分析和机器学习的当前潜力，以提取中风随机试验报告结果和结论的重要性。方法：对200份近期随机试验报告的PubMed摘要进行回顾，并根据研究的估计重要性进行手动分类。论文的重要性被划分为“游戏规则改变者”、“提示性”、“可能”、“负面结果”。随后，对“结果”和“结论”段落进行了算法情绪分析，得出了极性和主观性的数字输出。然后将人类评估的结果与极性和主观性进行比较。此外，使用基于Tensorflow和Python构建的Keras平台训练神经网络，将“结果”和“结论”映射到二分法人类评估（1：“游戏规则改变者”或“提示性”；0：“可能”或“负面”，或无结果报告）。120篇摘要作为训练集，80篇作为测试集。结果：200份报告中有9份被手动归类为“游戏规则改变者”，40份被归类为“暗示”，73份被归类为“可能”，32份被归类为“负面”；46篇摘要没有包含任何结果。“结论”的极性通常高于“结果”。被归类为“暗示性”的“结论”的极性最高。主观性在“暗示”和“可能”类中也高于在“游戏规则改变者”和“消极”类中。经过训练的神经网络提供了正确的二分法输出，基于“结果”的准确率为71%，基于“结论”的准确率为73%。结论：目前文本分析的统计方法在一定程度上可以把握科学医学摘要的影响。情绪分析表明，平庸的结果显然是用更热情的话写的，而不是明显的积极或消极的结果。（c） 2020爱思唯尔公司版权所有。

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