AIMC Topic: Natural Language Processing

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Auditing Learned Associations in Deep Learning Approaches to Extract Race and Ethnicity from Clinical Text.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Complete and accurate race and ethnicity (RE) patient information is important for many areas of biomedical informatics research, such as defining and characterizing cohorts, performing quality assessments, and identifying health inequities. Patient-...

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as t...

Automatic Mapping of Terminology Items with Transformers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Biomedical ontologies are a key component in many systems for the analysis of textual clinical data. They are employed to organize information about a certain domain relying on a hierarchy of different classes. Each class maps a concept to items in a...

Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models.

Chest
BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar trans...

Graph global attention network with memory: A deep learning approach for fake news detection.

Neural networks : the official journal of the International Neural Network Society
With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To add...

A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification.

Artificial intelligence in medicine
The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the u...

BactInt: A domain driven transfer learning approach for extracting inter-bacterial associations from biomedical text.

Computational biology and chemistry
BACKGROUND: The healthy as well as dysbiotic state of an ecosystem like human body is known to be influenced not only by the presence of the bacterial groups in it, but also with respect to the associations within themselves. Evidence reported in bio...

DeBERTa-BiLSTM: A multi-label classification model of Arabic medical questions using pre-trained models and deep learning.

Computers in biology and medicine
It is wise to investigate past and present epidemics in the hopes of profiting from them and being better prepared for future ones. COVID-19 is one of the most recent and well-known pandemics; its effects are still felt today. Most or nearly all gove...

Retrieval augmentation of large language models for lay language generation.

Journal of biomedical informatics
The complex linguistic structures and specialized terminology of expert-authored content limit the accessibility of biomedical literature to the general public. Automated methods have the potential to render this literature more interpretable to read...

Sharing Patient Praises With Radiology Staff: Workflow Automation and Impact on Staff.

Journal of the American College of Radiology : JACR
OBJECTIVE: This study aims to develop and evaluate a semi-automated workflow using natural language processing (NLP) for sharing positive patient feedback with radiology staff, assessing its efficiency and impact on radiology staff morale.