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Analysis of readability and structural accuracy in SNOMED CT.

BMC medical informatics and decision making
BACKGROUND: The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontologies, such as the Gene Ontolog...

Semi-supervised disentangled framework for transferable named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale la...

Transforming task representations to perform novel tasks.

Proceedings of the National Academy of Sciences of the United States of America
An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superh...

Predicting risk of dyslexia with an online gamified test.

PloS one
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified ...

Mixed-Level Neural Machine Translation.

Computational intelligence and neuroscience
Building the first Russian-Vietnamese neural machine translation system, we faced the problem of choosing a translation unit system on which source and target embeddings are based. Available homogeneous translation unit systems with the same translat...

Pre-training phenotyping classifiers.

Journal of biomedical informatics
Recent transformer-based pre-trained language models have become a de facto standard for many text classification tasks. Nevertheless, their utility in the clinical domain, where classification is often performed at encounter or patient level, is sti...

Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.

Journal of healthcare engineering
BACKGROUND: Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious...

Robot dramas may improve joint attention of Chinese-speaking low-functioning children with autism: stepped wedge trials.

Disability and rehabilitation. Assistive technology
INTRODUCTION: Children with autism spectrum disorder (ASD), especially those with low cognitive functioning, have deficits in joint attention. Previous research has found that these children are interested in engaging with social robots.

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients.

IEEE journal of biomedical and health informatics
Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone influence the meaning of the spoken utterance and overall speech clarity. ...

Cross domains adversarial learning for Chinese named entity recognition for online medical consultation.

Journal of biomedical informatics
Deep learning methods have been applied to Chinese named entity recognition for the online medical consultation. They require a large number of marked samples. However, no such database is available at present. This paper begins with constructing a l...