AIMC Topic: Natural Language Processing
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Understanding generative AI to harness its potentials and mini- mize risks: A perspective.
European journal of radiology
Jan 27, 2025
The hype surrounding Generative AI is such that the impression one may get is that these technologies are solving all the problems of humankind, including medical diagnoses. This can result in great disappointments (or worse) unless there is a clear ...
Natural language processing-based classification of early Alzheimer's disease from connected speech.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 27, 2025
INTRODUCTION: The automated analysis of connected speech using natural language processing (NLP) emerges as a possible biomarker for Alzheimer's disease (AD). However, it remains unclear which types of connected speech are most sensitive and specific...
EmoAtlas: An emotional network analyzer of texts that merges psychological lexicons, artificial intelligence, and network science.
Behavior research methods
Jan 27, 2025
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically v...
Semiautomated Extraction of Research Topics and Trends From National Cancer Institute Funding in Radiological Sciences From 2000 to 2020.
International journal of radiation oncology, biology, physics
Jan 25, 2025
PURPOSE: Investigators and funding organizations desire knowledge on topics and trends in publicly funded research but current efforts for manual categorization have been limited in breadth and depth of understanding. We present a semiautomated analy...
Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.
European journal of nuclear medicine and molecular imaging
Jan 23, 2025
PURPOSE: The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system...
Discontinuous named entities in clinical text: A systematic literature review.
Journal of biomedical informatics
Jan 23, 2025
OBJECTIVE: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identi...
Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.
JMIR cancer
Jan 23, 2025
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...
Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.
Computers in biology and medicine
Jan 21, 2025
Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these conditions is crucial for timely intervention and prevention of adverse outcomes. In this study, we...
Large language models vs human for classifying clinical documents.
International journal of medical informatics
Jan 21, 2025
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
Jan 21, 2025
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 ...
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