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Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.

BMC bioinformatics
BACKGROUND AND OBJECTIVE: Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners...

Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls.

Frontiers in endocrinology
BACKGROUND AND OBJECTIVES: As the worldwide secular trends are toward earlier puberty, identification of contributing factors for precocious puberty is critical. We aimed to identify and optimize contributing factors responsible for onset of precocio...

Microblog User Emotion Analysis Method Based on Improved Hierarchical Attention Mechanism and BiLSTM.

Computational intelligence and neuroscience
The goal of Chinese fine-grained emotion analysis is to identify the target words corresponding to fine-grained elements from sentences and determine the corresponding emotional polarity for the target words. Aiming at the problem that the current Si...

Simulation of English Word Order Sorting Based on Semionline Model and Artificial Intelligence.

Computational intelligence and neuroscience
To improve the word order ranking effect of English language retrieval, based on machine learning algorithms, this paper combines a semionline model to construct an artificial intelligence ranking model for English word order based on a semionline mo...

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research.

Journal of biomedical semantics
BACKGROUND: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and met...

A hybrid method based on semi-supervised learning for relation extraction in Chinese EMRs.

BMC medical informatics and decision making
BACKGROUND: Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification...

A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text.

Computational intelligence and neuroscience
Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantl...

Construction and Research on Chinese Semantic Mapping Based on Linguistic Features and Sparse Self-Learning Neural Networks.

Computational intelligence and neuroscience
In this paper, we adopt the algorithms of linguistic feature Rong and sparse self-learning neural network to conduct an in-depth study and analysis of Chinese semantic mapping, which complements the emotion semantic representation ability of traditio...

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction.

Computational intelligence and neuroscience
How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently amelior...

Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition.

Sensors (Basel, Switzerland)
Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing pe...