AIMC Topic: Natural Language Processing
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A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.
Headache
Mar 25, 2024
OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes.
Enhancing Patient Safety in Prehospital Environment: Analyzing Patient Perspectives on Non-Transport Decisions With Natural Language Processing and Machine Learning.
Journal of patient safety
Mar 23, 2024
OBJECTIVE: This research explored the experiences and perspectives of patients declining hospital transportation after receiving prehospital emergency care using advanced computational techniques.
Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline.
Artificial intelligence in medicine
Mar 22, 2024
Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes...
Development of method using language processing techniques for extracting information on drug-health food product interactions.
British journal of clinical pharmacology
Mar 20, 2024
AIMS: Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions ...
De-identification of clinical free text using natural language processing: A systematic review of current approaches.
Artificial intelligence in medicine
Mar 20, 2024
BACKGROUND: Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the p...
Practical use case of natural language processing for observational clinical research data retrieval from electronic health records: AssistMED project.
Polish archives of internal medicine
Mar 19, 2024
INTRODUCTION: Electronic health records (EHRs) contain data valuable for clinical research. However, they are in textual format and require manual encoding to databases, which is a lengthy and costly process. Natural language processing (NLP) is a co...
Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022.
Journal of medical Internet research
Mar 19, 2024
BACKGROUND: In recent years, Korean society has increasingly recognized the importance of nurses in the context of population aging and infectious disease control. However, nurses still face difficulties with regard to policy activities that are aime...
Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department.
The American journal of emergency medicine
Mar 16, 2024
OBJECTIVE: To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax.
Journal of imaging informatics in medicine
Mar 14, 2024
Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image a...
Can large language models replace humans in systematic reviews? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages.
Research synthesis methods
Mar 14, 2024
Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be ...
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