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Large Language Models

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Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.

JMIR cancer
BACKGROUND: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology...

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.

Journal of medical Internet research
BACKGROUND: Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to e...

Large language models vs human for classifying clinical documents.

International journal of medical informatics
BACKGROUND: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and...

Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured ...

Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review.

JMIR research protocols
BACKGROUND: In recent years, the intersection of natural language processing (NLP) and public health has opened innovative pathways for investigating social determinants of health (SDOH) in textual datasets. Despite the promise of NLP in the SDOH dom...

Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation.

BMJ health & care informatics
OBJECTIVES: We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.

Extraction and classification of structured data from unstructured hepatobiliary pathology reports using large language models: a feasibility study compared with rules-based natural language processing.

Journal of clinical pathology
AIMS: Structured reporting in pathology is not universally adopted and extracting elements essential to research often requires expensive and time-intensive manual curation. The accuracy and feasibility of using large language models (LLMs) to extrac...

Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

Prehospital emergency care
OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Larg...

Performance Evaluation and Implications of Large Language Models in Radiology Board Exams: Prospective Comparative Analysis.

JMIR medical education
BACKGROUND: Artificial intelligence advancements have enabled large language models to significantly impact radiology education and diagnostic accuracy.

A Comprehensive Analysis of a Social Intelligence Dataset and Response Tendencies Between Large Language Models (LLMs) and Humans.

Sensors (Basel, Switzerland)
In recent years, advancements in the interaction and collaboration between humans and have garnered significant attention. Social intelligence plays a crucial role in facilitating natural interactions and seamless communication between humans and Art...