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Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study.
J Med Internet Res
January 2025
Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States.
Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored.
View Article and Find Full Text PDFLearning too much from too little: False face stereotypes emerge from a few exemplars and persist via insufficient sampling.
J Pers Soc Psychol
January 2025
Booth School of Business, The University of Chicago.
Face stereotypes are prevalent, consequential, yet oftentimes inaccurate. How do false first impressions arise and persist despite counter-evidence? Building on the overgeneralization hypothesis, we propose a domain-general cognitive mechanism: insufficient statistical learning, or Insta-learn. This mechanism posits that humans are quick statistical learners but insufficient samplers.
View Article and Find Full Text PDFIdentifying metabolomic mediators of the physical activity and colorectal cancer relationship.
Cancer Epidemiol Biomarkers Prev
January 2025
University of Bristol, Bristol, United Kingdom.
Background: Current evidence suggests higher physical activity (PA) levels are associated with a reduced risk of colorectal cancer (CRC). However, the mediating role of the circulating metabolome in this relationship remains unclear.
Methods: Targeted metabolomics data from 6,055 participants in the EPIC cohort were used to identify metabolites associated with PA and derive a metabolomic signature of PA levels.
Ejtm3 experiences after ChatGPT and other AI approaches: values, risks, countermeasures.
Eur J Transl Myol
January 2025
A&C M-C Foundation for Translational Myology, Padua, Italy; Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Digital Transformation, Landspitali University Hospital, Reykjavík.
We invariably hear that Artificial Intelligence (AI), a rapidly evolving technology, does not just creatively assemble known knowledge. We are told that AI learns, processes and creates, starting from fixed points to arrive at innovative solutions. In the case of scientific work, AI can generate data without ever having entered a laboratory, (i.
View Article and Find Full Text PDFAugmenting motion artifacts to enhance auto-contouring of complex structures in cone-beam computed tomography imaging.
Phys Med Biol
January 2025
Imaging Laboratory (iLab), Varian Medical Systems, Siemens Healthcare, Baden, Switzerland.
. To develop an augmentation method that simulates cone-beam computed tomography (CBCT) related motion artifacts, which can be used to generate training-data to increase the performance of artificial intelligence models dedicated to auto-contouring tasks.The augmentation technique generates data that simulates artifacts typically present in CBCT imaging.
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