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Disease vocabulary size as a surrogate marker for physicians' disease knowledge volume.

PloS one
OBJECTIVE: Recognizing what physicians know and do not know about a particular disease is one of the keys to designing clinical decision support systems, since these systems can fulfill complementary role by recognizing this boundary. To our knowledg...

A lexicon based method to search for extreme opinions.

PloS one
Studies in sentiment analysis and opinion mining have been focused on many aspects related to opinions, namely polarity classification by making use of positive, negative or neutral values. However, most studies have overlooked the identification of ...

Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

International journal of neural systems
Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding ...

Too Much of a Good Thing: How Novelty Biases and Vocabulary Influence Known and Novel Referent Selection in 18-Month-Old Children and Associative Learning Models.

Cognitive science
Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word-referent link. Prior work i...

Identifying tweets of personal health experience through word embedding and LSTM neural network.

BMC bioinformatics
BACKGROUND: As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorit...

Interactive medical word sense disambiguation through informed learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert's domain kno...

EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.

BMC medical informatics and decision making
BACKGROUND: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive ...

RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases.

Computational intelligence and neuroscience
The use of intelligent judgment technology to assist in judgment is an inevitable trend in the development of judgment in contemporary social legal cases. Using big data and artificial intelligence technology to accurately determine multiple accusati...

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks.

BMC bioinformatics
BACKGROUND: Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks. The adven...

MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions.

Bioinformatics (Oxford, England)
MOTIVATION: A major component in increasing our understanding of the biology of an organism is the mapping of its genotypic potential into its phenotypic expression profiles. This mapping is executed by the machinery of gene regulation, which is esse...