AIMC Topic: Natural Language Processing

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An explainable RoBERTa approach to analyzing panic and anxiety sentiment in oral health education YouTube comments.

Scientific reports
Online videos are vital for health education and medical decision-making, but their comment sections often spread misinformation, causing anxiety and confusion. This study identifies stress-inducing comments in oral health education content, aiming t...

Sentiment classification for telugu using transformed based approaches on a multi-domain dataset.

Scientific reports
Sentiment analysis is an essential component of Natural Language Processing (NLP) in resource-abundant languages such as English. Nevertheless, poor-resource languages such as Telugu have experienced limited efforts owing to multiple considerations, ...

A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.

Scientific data
The advancement of clinical natural language processing systems is crucial to exploit the wealth of textual data contained in medical records. Diverse data sources are required in different languages and from different sites to represent global healt...

Analyzing Patient Complaints in Web-Based Reviews of Private Hospitals in Selangor, Malaysia, Using Large Language Model-Assisted Content Analysis: Mixed Methods Study.

JMIR formative research
BACKGROUND: Large language model (LLM)-assisted content analysis (LACA) is a modification of traditional content analysis, leveraging the LLM to codevelop codebooks and automatically assign thematic codes to a web-based reviews dataset.

How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analyses.

Journal of biomedical informatics
OBJECTIVE: This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore lev...

Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism.

PloS one
Improving translation quality and efficiency is one of the key challenges in the field of Natural Language Processing (NLP). This study proposes an enhanced model based on Bidirectional Encoder Representations from Transformers (BERT), combined with ...

Improving personalized healthcare with automated longitudinal EHR analysis.

International journal of medical informatics
BACKGROUND: Traditional Electronic Health Record (EHR) data analysis at King's College Hospital relies on extensive manual effort, from data extraction to reporting, limiting efficiency and scalability. This study presents an automated framework for ...

A flexible two-stage anonymization framework for narrative medical records adapting to various language models.

Computers in biology and medicine
The healthcare sector increasingly relies on Electronic Health Records (EHRs) for efficient and high-quality patient care by providing rapid access to comprehensive medical information. However, these records contain sensitive patient data that must ...

Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models.

Computers in biology and medicine
Lung cancer remains the leading cause of cancer deaths in many developed countries, primarily due to late-stage diagnosis. Histopathology, the gold standard for diagnosis, often results in semi-structured pathological reports containing complex infor...

SSMT-PANBERT: A single-stage multitask model for phenotype extraction and assertion negation detection in unstructured clinical text.

Computers in biology and medicine
Automatic phenotype extraction and assertion negation detection from large-scale accessible Electronic Health Records (EHRs), including discharge summaries and radiology reports, is a crucial task for various healthcare applications, such as disease ...