AIMC Topic: Natural Language Processing

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Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.

Annals of vascular surgery
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
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
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
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
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
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...

Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.

Journal of applied gerontology : the official journal of the Southern Gerontological Society
This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) ...

MV-SHIF: Multi-view symmetric hypothesis inference fusion network for emotion-cause pair extraction in documents.

Neural networks : the official journal of the International Neural Network Society
Emotion-cause pair extraction (ECPE) is a challenging task that aims to automatically identify pairs of emotions and their causes from documents. The difficulty of ECPE lies in distinguishing valid emotion-cause pairs from many irrelevant ones. Most ...

Calculated Medicine: Seven Decades of Accelerating Growth.

The American journal of medicine
The field of Calculated Medicine has grown substantially over the last 7 decades. Comprised of objective, evidence-based medical decision tools, Calculated Medicine has broad application in medical practice, medical research, and health care manageme...

Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.

Japanese journal of radiology
PURPOSE: To propose a five-point scale for radiology report importance called Report Importance Category (RIC) and to compare the performance of natural language processing (NLP) algorithms in assessing RIC using head computed tomography (CT) reports...