AI Medical Compendium Topic:
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When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.

BMC medical informatics and decision making
BACKGROUND: Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers ...

Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.

Computational intelligence and neuroscience
Emotions play an essential role in human relationships, and many real-time applications rely on interpreting the speaker's emotion from their words. Speech emotion recognition (SER) modules aid human-computer interface (HCI) applications, but they ar...

PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

Journal of biomedical informatics
The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our mo...

A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.

Journal of medical Internet research
BACKGROUND: Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems used to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growi...

A BERT based dual-channel explainable text emotion recognition system.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel dual-channel system for multi-class text emotion recognition has been proposed, and a novel technique to explain its training & predictions has been developed. The architecture of the proposed system contains the embedding modu...

Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF.

Artificial intelligence in medicine
Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinica...

Deep learning-based methods for natural hazard named entity recognition.

Scientific reports
Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide...

Decoding lip language using triboelectric sensors with deep learning.

Nature communications
Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we pro...

English Text Readability Measurement Based on Convolutional Neural Network: A Hybrid Network Model.

Computational intelligence and neuroscience
Text readability is very important in meeting people's information needs. With the explosive growth of modern information, the measurement demand of text readability is increasing. In view of the text structure of words, sentences, and texts, a hybri...

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts.

Computational intelligence and neuroscience
Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for dig...