AIMC Topic: Natural Language Processing

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GDReCo: Fine-grained gene-disease relationship extraction corpus.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Understanding gene-disease relationships is crucial for medical research, drug discovery, clinical diagnosis, and other fields. However, there is currently no high-quality, fine-grained corpus available for training Natural ...

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study aims to develop and evaluate an approach using large language models (LLMs) and a knowledge graph to triage patient messages that need emergency care. The goal is to notify patients when their messages indicate an emergency, gu...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

SLAS technology
The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Mode...

Large language models in breast cancer reconstruction: A framework for patient-specific recovery and predictive insights.

SLAS technology
Breast cancer reconstruction, a vital part of comprehensive cancer therapy, can be performed concurrently with cancer resection, improving both physical and psychological recovery for patients. However, the intricacy and variety of recovery demand a ...

Automating the Addiction Behaviors Checklist for Problematic Opioid Use Identification.

JAMA psychiatry
IMPORTANCE: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (EHR) allow large-scale studies to identify a continuum of problematic opioid use, including opioid us...

Integrating large language models with human expertise for disease detection in electronic health records.

Computers in biology and medicine
OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelli...

Evaluating the effectiveness of biomedical fine-tuning for large language models on clinical tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this approach remains unclear. This study aims to critically evaluat...

DiMB-RE: mining the scientific literature for diet-microbiome associations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To develop a corpus annotated for diet-microbiome associations from the biomedical literature and train natural language processing (NLP) models to identify these associations, thereby improving the understanding of their role in health a...

On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook.

IEEE journal of biomedical and health informatics
Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the researc...

The Large Language Models on Biomedical Data Analysis: A Survey.

IEEE journal of biomedical and health informatics
With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent ...