AIMC Topic: Natural Language Processing

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De-identification of clinical free text using natural language processing: A systematic review of current approaches.

Artificial intelligence in medicine
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
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
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...

ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax.

Journal of imaging informatics in medicine
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
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 ...

Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.

Internal medicine journal
BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data...

Identification of clinical disease trajectories in neurodegenerative disorders with natural language processing.

Nature medicine
Neurodegenerative disorders exhibit considerable clinical heterogeneity and are frequently misdiagnosed. This heterogeneity is often neglected and difficult to study. Therefore, innovative data-driven approaches utilizing substantial autopsy cohorts ...

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Clinical pharmacology and therapeutics
Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidir...