We present an approach to automatically classify clinical text at a sentence
level. We are using deep convolutional neural networks to represent complex
features. We train the network on a dataset providing a broad categorization of
health information. Through a detailed evaluation, we demonstrate that our
method outperforms several approaches widely used in natural language
processing tasks by about 15%.

研究应用深度卷积神经网络自动对临床文本进行分类，并证明该方法在自然语言处理任务中比多种广泛应用的方法提高约 15% 的准确性。

卷积神经网络在医学文本分类中的应用

Medical Text Classification using Convolutional Neural Networks

In this paper, we report a knowledge-based method for Word Sense
Disambiguation in the domains of biomedical and clinical text. We combine word
representations created on large corpora with a small number of definitions
from the UMLS to create concept representations, which we then compare to
representations of the context of ambiguous terms. Using no relational
information, we obtain comparable performance to previous approaches on the
MSH-WSD dataset, which is a well-known dataset in the biomedical domain.
Additionally, our method is fast and easy to set up and extend to other
domains. Supplementary materials, including source code, can be found at https:
//github.com/clips/yarn

本文报告了一种在生物医学和临床文本领域中进行词义消歧的基于知识的方法。我们结合大规模语料库上创建的词表示和来自 UMLS 的少量定义创建概念表示，然后将其与含糊术语上下文的表示进行比较。使用无关系信息，我们在生物医学领域中已知的数据集（MSH-WSD 数据集）上获得了可比较的性能。此外，我们的方法快速、易于设置和扩展到其他领域。