基于机器学习的临床文本数据检测COVID-19方法

Akib Mohi Ud Din Khanday, Syed Tanzeel Rabani, Qamar Rayees Khan, Nusrat Rouf, Masarat Mohi Ud Din
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引用次数: 219

摘要

技术进步对生活的各个领域都产生了迅速的影响,无论是医疗领域还是其他领域。人工智能通过分析数据做出决策,在医疗保健领域显示出了令人鼓舞的成果。新冠肺炎疫情在短时间内影响了100多个国家。世界各地的人们在未来都很容易受到其后果的影响。当务之急是开发一种能够检测冠状病毒的控制系统。控制当前浩劫的解决方案之一可能是借助各种人工智能工具进行疾病诊断。在本文中,我们使用经典和集成机器学习算法将文本临床报告分为四类。特征工程使用术语频率/逆文档频率(TF/IDF)、词包(BOW)和报告长度等技术进行。这些特征提供给传统和集成机器学习分类器。Logistic回归和多项式Naïve Bayes的测试准确率达到96.2%,优于其他ML算法。在未来,递归神经网络可以获得更好的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning based approaches for detecting COVID-19 using clinical text data.

Machine learning based approaches for detecting COVID-19 using clinical text data.

Machine learning based approaches for detecting COVID-19 using clinical text data.

Machine learning based approaches for detecting COVID-19 using clinical text data.

Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.

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