AIMC Topic: Natural Language Processing

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Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.

PloS one
OBJECTIVE: To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging.

Getting to Know Named Entity Recognition: Better Information Retrieval.

Medical reference services quarterly
Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant ...

What do you think caused your ALS? An analysis of the CDC national amyotrophic lateral sclerosis patient registry qualitative risk factor data using artificial intelligence and qualitative methodology.

Amyotrophic lateral sclerosis & frontotemporal degeneration
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is an incurable, progressive neurodegenerative disease with a significant health burden and poorly understood etiology. This analysis assessed the narrative responses from 3,061 participants in the Cente...

Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies.

The American journal of Chinese medicine
Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the abse...

An artificial intelligence-based dental semantic search engine as a reliable tool for dental students and educators.

Journal of dental education
PURPOSE/OBJECTIVES: This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education while addressing the increasing concerns of accuracy, bias, and hallucination in outputs generated b...

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study.

Anaesthesia, critical care & pain medicine
BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the...

Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis.

International journal of public health
Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a...

Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT.

Artificial intelligence in medicine
BACKGROUND: Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirection...

A Dataset for Evaluating Contextualized Representation of Biomedical Concepts in Language Models.

Scientific data
Due to the complexity of the biomedical domain, the ability to capture semantically meaningful representations of terms in context is a long-standing challenge. Despite important progress in the past years, no evaluation benchmark has been developed ...

Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis.

JMIR mental health
BACKGROUND: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied ...