With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequence-l...
OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classif...
BMC medical informatics and decision making
Dec 23, 2019
BACKGROUND: Clinical Named Entity Recognition is to find the name of diseases, body parts and other related terms from the given text. Because Chinese language is quite different with English language, the machine cannot simply get the graphical and ...
BMC medical informatics and decision making
Dec 23, 2019
BACKGROUND: Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigat...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms f...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of h...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
Neither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achie...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely as...
OBJECTIVE: We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities.
BMC medical informatics and decision making
Dec 5, 2019
BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon...
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