AIMC Topic: National Institutes of Health (U.S.)
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Use of machine learning models to identify National Institutes of Health-funded cardiac arrest research.
Resuscitation
Feb 15, 2025
OBJECTIVE: To compare the performance of three artificial intelligence (AI) classification strategies against manually classified National Institutes of Health (NIH) cardiac arrest (CA) grants, with the goal of developing a publicly available tool to...
An extremely lightweight CNN model for the diagnosis of chest radiographs in resource-constrained environments.
Medical physics
Sep 4, 2023
BACKGROUND: In recent years, deep learning methods have been successfully used for chest x-ray diagnosis. However, such deep learning models often contain millions of trainable parameters and have high computation demands. As a result, providing the ...
Artificial intelligence at the national eye institute.
Current opinion in ophthalmology
Jul 16, 2022
PURPOSE OF REVIEW: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goa...
Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative.
Arthritis care & research
Apr 1, 2022
OBJECTIVE: To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of H...
Translatability Analysis of National Institutes of Health-Funded Biomedical Research That Applies Artificial Intelligence.
JAMA network open
Jan 4, 2022
IMPORTANCE: Despite the rapid growth of interest and diversity in applications of artificial intelligence (AI) to biomedical research, there are limited objective ways to characterize the potential for use of AI in clinical practice.
Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
PloS one
Aug 11, 2021
Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, lear...
A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.
Osteoarthritis and cartilage
Apr 16, 2019
OBJECTIVE: Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progressi...
Databases, Factual
Biomarkers
Female
Middle Aged
Disease Progression
Aged
Male
Congresses as Topic
Humans
National Institutes of Health (U.S.)
Machine Learning
Biological Variation, Population
Severity of Illness Index
Osteoarthritis, Knee
United States
Cartilage, Articular
Collagen Type II
Pain Measurement
Menisci, Tibial
A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.
International journal of medical informatics
Feb 20, 2019
OBJECTIVE: In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT).
A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research.
American journal of preventive medicine
Oct 25, 2018
INTRODUCTION: To fulfill its mission, the NIH Office of Disease Prevention systematically monitors NIH investments in applied prevention research. Specifically, the Office focuses on research in humans involving primary and secondary prevention, and ...
Machine learning reveals chronic graft--host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.
Haematologica
Sep 20, 2018
The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft--host disease. Chronic graft--host disease is classified by an...
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