AIMC Topic: Natural Language Processing
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Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.
Journal of biomedical informatics
Apr 10, 2024
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge ...
Pipelined biomedical event extraction rivaling joint learning.
Methods (San Diego, Calif.)
Apr 9, 2024
Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopt...
Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question measurements.
Scientific reports
Apr 9, 2024
Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual's attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental...
Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review.
American journal of infection control
Apr 6, 2024
BACKGROUND: Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention.
Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.
Annals of vascular surgery
Apr 4, 2024
Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various appli...
Evaluation of large language models performance against humans for summarizing MRI knee radiology reports: A feasibility study.
International journal of medical informatics
Apr 4, 2024
OBJECTIVES: This study addresses the critical need for accurate summarization in radiology by comparing various Large Language Model (LLM)-based approaches for automatic summary generation. With the increasing volume of patient information, accuratel...
Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.
Computers in biology and medicine
Apr 4, 2024
Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived from tumor cells, are presented to immune cells (T cells) through major histocompatibility complex ...
EndoViT: pretraining vision transformers on a large collection of endoscopic images.
International journal of computer assisted radiology and surgery
Apr 3, 2024
PURPOSE: Automated endoscopy video analysis is essential for assisting surgeons during medical procedures, but it faces challenges due to complex surgical scenes and limited annotated data. Large-scale pretraining has shown great success in natural l...
Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Apr 3, 2024
BACKGROUND: Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text su...
Classifying early infant feeding status from clinical notes using natural language processing and machine learning.
Scientific reports
Apr 3, 2024
The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classi...
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