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...
BMC medical informatics and decision making
Dec 5, 2019
BACKGROUND: Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful relationships between entities au...
This paper offers a new way of considering places as special types of categories, in human cognition of larger-scale environments. This may provide an explanatory cognitive model for a range of known phenomena from environmental psychology and human ...
Traditional general linear model-based brain mapping efforts using functional neuroimaging are complemented by more recent multivariate pattern analyses (MVPA) that apply machine learning techniques to identify the cognitive states associated with re...
Causal graphs play an essential role in the determination of causalities and have been applied in many domains including biology and medicine. Traditional causal graph construction methods are usually data-driven and may not deliver the desired accur...
In this paper, using Word2vec, a widely-used natural language processing method, we demonstrate that protein domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to the multi-domain proteins in whi...
Colorectal polyps are known to be potential precursors to colorectal cancer, which is one of the leading causes of cancer-related deaths on a global scale. Early detection and prevention of colorectal cancer is primarily enabled through manual screen...
Semantic universals are properties of meaning shared by the languages of the world. We offer an explanation of the presence of such universals by measuring simplicity in terms of ease of learning, showing that expressions satisfying universals are si...
With the increase in the amount of text information in different real-life applications, automatic text-summarization systems become more predominant in extracting relevant information. In the current study, we formulated the problem of extractive te...
BACKGROUND: Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge...
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