Moen Hans, Peltonen Laura-Maria, Koivumäki Mikko, Suhonen Henry, Salakoski Tapio, Ginter Filip, Salanterä Sanna
Turku NLP Group, Department of Future Technologies, University of Turku, Finland.
Department of Nursing Science, University of Turku, Finland.
Stud Health Technol Inform. 2018;247:725-729.
We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled text - a clinical corpus and a general domain corpus. A joint semantic word-space model is created for the purpose of extracting easier to understand alternatives for words considered difficult to understand by laymen. Two domain experts evaluate the tool and inter-rater agreement is calculated. When having the tool suggest ten alternatives to each difficult word, it suggests acceptable lay words for 55.51% of them. This and future manual evaluation will serve to further improve performance, where also supervised machine learning will be used.
我们报告了一个原型工具的开发与评估情况，该工具旨在帮助外行人/患者理解临床叙述的内容。该工具主要依赖于应用于两个未标记文本大语料库（一个临床语料库和一个通用领域语料库）的无监督机器学习。为了提取外行人认为难以理解的单词的更易理解的替代词，创建了一个联合语义词空间模型。两名领域专家对该工具进行评估，并计算评分者间信度。当让该工具为每个难词提供十个替代词时，它为其中55.51%的词提供了可接受的常用词。此次及未来的人工评估将有助于进一步提高性能，同时也将使用监督机器学习。
