AIMC Topic: Language

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GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models.

Computers in biology and medicine
Recently, language representation models have drawn a lot of attention in the field of natural language processing (NLP) due to their remarkable results. Among them, BERT (Bidirectional Encoder Representations from Transformers) has proven to be a si...

Neural Encoding and Decoding With Distributed Sentence Representations.

IEEE transactions on neural networks and learning systems
Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, ha...

Patient Cohort Retrieval using Transformer Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a g...

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading cause...

Design and Implementation of Fast Spoken Foul Language Recognition with Different End-to-End Deep Neural Network Architectures.

Sensors (Basel, Switzerland)
Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual's character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensor...

Exploration of text matching methods in Chinese disease Q&A systems: A method using ensemble based on BERT and boosted tree models.

Journal of biomedical informatics
BACKGROUND: Text matching is one of the basic tasks in the field of natural language processing. Owing to the particularity of Chinese language and medical texts, text matching has greater application and research value in the medical field. In 2019,...

ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations.

Journal of molecular biology
The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated f...

BertMCN: Mapping colloquial phrases to standard medical concepts using BERT and highway network.

Artificial intelligence in medicine
In the last few years, people started to share lots of information related to health in the form of tweets, reviews and blog posts. All these user generated clinical texts can be mined to generate useful insights. However, automatic analysis of clini...

Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.

BMC medical informatics and decision making
BACKGROUND: Next-generation sequencing provides comprehensive information about individuals' genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not ...

Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network.

PloS one
The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides ...