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Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.
Lung Cancer
January 2025
Dept. of Medical Oncology, Princess Margaret Cancer Center, Toronto, ON, Canada.
Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.
View Article and Find Full Text PDFTwenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.
J Med Internet Res
January 2025
Chronic Disease Epidemiology, Population and Public Health, Pennington Biomedical Research Center, Baton Rouge, LA, United States.
Background: Electronic health records (EHRs) facilitate the accessibility and sharing of patient data among various health care providers, contributing to more coordinated and efficient care.
Objective: This study aimed to summarize the evolution of secondary use of EHRs and their interoperability in medical research over the past 25 years.
Methods: We conducted an extensive literature search in the PubMed, Scopus, and Web of Science databases using the keywords Electronic health record and Electronic medical record in the title or abstract and Medical research in all fields from 2000 to 2024.
Vanin-1-Activated Fluorescent Probe for Real-Time Imaging of Inflammatory Responses Across Multiple Tissue Types.
Anal Chem
January 2025
College of Chemistry, Jilin Province Research Center for Engineering and Technology of Spectral Analytical Instruments, Jilin University, Qianjin Street 2699, Changchun 130012, China.
Vanin-1 is a pantetheine hydrolase that plays a key role in inflammatory diseases. Effective tools for noninvasive, real-time monitoring of Vanin-1 are lacking, largely due to background fluorescence interference in existing probes. To address this issue, we developed a dual-modal fluorescent and colorimetric probe, MB-Van1, to detect Vanin-1 with high sensitivity and selectivity.
View Article and Find Full Text PDFToward a Free-Response Paradigm of Decision-Making in Spiking Neural Networks.
Neural Comput
January 2025
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200437, China
Spiking neural networks (SNNs) have attracted significant interest in the development of brain-inspired computing systems due to their energy efficiency and similarities to biological information processing. In contrast to continuous-valued artificial neural networks, which produce results in a single step, SNNs require multiple steps during inference to achieve a desired accuracy level, resulting in a burden in real-time response and energy efficiency. Inspired by the tradeoff between speed and accuracy in human and animal decision-making processes, which exhibit correlations among reaction times, task complexity, and decision confidence, an inquiry emerges regarding how an SNN model can benefit by implementing these attributes.
View Article and Find Full Text PDFUncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm.
Neural Comput
January 2025
Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, BT48 7JL Derry-Londonderry, Northern Ireland, U.K.
Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation.
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