BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine [0.03%]
生物医学中的动词语义语法分类法BioVerbNet
Olga Majewska,Charlotte Collins,Simon Baker et al.
Olga Majewska et al.
Background: Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structur...
Synthetic data for annotation and extraction of family history information from clinical text [0.03%]
合成数据在临床文本中家系史信息抽取与标注中的应用研究
Pål H Brekke,Taraka Rama,Ildikó Pilán et al.
Pål H Brekke et al.
Background: The limited availability of clinical texts for Natural Language Processing purposes is hindering the progress of the field. This article investigates the use of synthetic data for the annotation and automated ...
Learning adaptive representations for entity recognition in the biomedical domain [0.03%]
用于生物医学领域的实体识别的自适应表示学习方法研究
Ivano Lauriola,Fabio Aiolli,Alberto Lavelli et al.
Ivano Lauriola et al.
Background: Named Entity Recognition is a common task in Natural Language Processing applications, whose purpose is to recognize named entities in textual documents. Several systems exist to solve this task in the biomedi...
Ayeh Naghizadeh,Mahdi Salamat,Donya Hamzeian et al.
Ayeh Naghizadeh et al.
Background: Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unifi...
Project Rosetta: a childhood social, emotional, and behavioral developmental feature mapping [0.03%]
罗塞塔计划——儿童社交、情感和行为发展特征绘图项目
Alyson Maslowski,Halim Abbas,Kelley Abrams et al.
Alyson Maslowski et al.
Background: A wide array of existing instruments are commonly used to assess childhood behavior and development for the evaluation of social, emotional and behavioral disorders such as Autism Spectrum Disorder (ASD), atte...
Improved characterisation of clinical text through ontology-based vocabulary expansion [0.03%]
基于本体的词汇扩充在临床文本特点描述中的应用研究
Luke T Slater,William Bradlow,Simon Ball et al.
Luke T Slater et al.
Background: Biomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the ...
De-identifying Spanish medical texts - named entity recognition applied to radiology reports [0.03%]
应用于放射科报告的命名实体识别——西班牙语医学文本去标识化
Irene Pérez-Díez,Raúl Pérez-Moraga,Adolfo López-Cerdán et al.
Irene Pérez-Díez et al.
Background: Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal informat...
SIENA: Semi-automatic semantic enhancement of datasets using concept recognition [0.03%]
使用概念识别的半自动语义数据集增强（SIENA）
Andreea Grigoriu,Amrapali Zaveri,Gerhard Weiss et al.
Andreea Grigoriu et al.
Background: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when...
Why and how to engage expert stakeholders in ontology development: insights from social and behavioural sciences [0.03%]
为何以及如何在本体开发过程中吸引专家利益相关者：来自社会和行为科学的启示
Emma Norris,Janna Hastings,Marta M Marques et al.
Emma Norris et al.
Background: Incorporating the feedback of expert stakeholders in ontology development is important to ensure content is appropriate, comprehensive, meets community needs and is interoperable with other ontologies and clas...
Haiyang Yang,Li Kuang,FengQiang Xia
Haiyang Yang
Background: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and ...