AIMC Topic: Natural Language Processing
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Language model based on deep learning network for biomedical named entity recognition.
Methods (San Diego, Calif.)
Apr 17, 2024
Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedica...
Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing.
Journal of racial and ethnic health disparities
Apr 16, 2024
PURPOSE: This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processin...
Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.
Neural networks : the official journal of the International Neural Network Society
Apr 16, 2024
In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential of multi-modal documents incorporating images. To address this gap, we introduce an innovative appro...
How artificial intelligence could transform emergency care.
The American journal of emergency medicine
Apr 16, 2024
Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare appl...
Unsupervised Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering.
Neural networks : the official journal of the International Neural Network Society
Apr 15, 2024
Pre-trained Language Model (PLM) is nowadays the mainstay of Unsupervised Sentence Representation Learning (USRL). However, PLMs are sensitive to the frequency information of words from their pre-training corpora, resulting in anisotropic embedding s...
Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.
Systematic reviews
Apr 15, 2024
BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This stud...
Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.
Journal of biomedical informatics
Apr 14, 2024
OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different dise...
Application of natural language processing to predict final recommendation of Brazilian health technology assessment reports.
International journal of technology assessment in health care
Apr 12, 2024
INTRODUCTION: Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies.
Machine Learning for Predictive Analysis of Otolaryngology Residency Letters of Recommendation.
The Laryngoscope
Apr 11, 2024
INTRODUCTION: Letters of recommendation (LORs) are a highly influential yet subjective and often enigmatic aspect of the residency application process. This study hypothesizes that LORs do contain valuable insights into applicants and can be used to ...
KEGG orthology prediction of bacterial proteins using natural language processing.
BMC bioinformatics
Apr 11, 2024
BACKGROUND: The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by ...
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