AIMC Topic: National Institutes of Health (U.S.)

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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...
Humans Machine Learning Prospective Studies Adult Hematologic Neoplasms Biomarkers Female Transplantation, Homologous National Institutes of Health (U.S.) United States Male Graft vs Host Disease Chronic Disease Middle Aged Hematopoietic Stem Cell Transplantation Consensus
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ProvCaRe Semantic Provenance Knowledgebase: Evaluating Scientific Reproducibility of Research Studies.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Scientific reproducibility is critical for biomedical research as it enables us to advance science by building on previous results, helps ensure the success of increasingly expensive drug trials, and allows funding agencies to make informed decisions...
Humans Algorithms Reproducibility of Results Biological Ontologies Sleep Biomedical Research Health Services Research Metadata Guidelines as Topic United States Semantics National Institutes of Health (U.S.) Knowledge Bases
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NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

Journal of biomedical semantics Jun 7, 2017
BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across dispara...
United States Semantics Biological Ontologies National Institutes of Health (U.S.)
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Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
Scientific reproducibility is key to scientific progress as it allows the research community to build on validated results, protect patients from potentially harmful trial drugs derived from incorrect results, and reduce wastage of valuable resources...
National Institutes of Health (U.S.) Humans Metadata Reproducibility of Results United States Databases as Topic Biomedical Research Semantics Sleep Biological Ontologies Sleep Wake Disorders
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KnowEnG: a knowledge engine for genomics.

Journal of the American Medical Informatics Association : JAMIA Jul 23, 2015
We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of "Knowled...
Knowledge Bases Humans Datasets as Topic Genomics National Institutes of Health (U.S.) United States
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New NIH Program for Artificial Intelligence in Research.

JAMA Oct 25, 2022
Artificial Intelligence Biomedical Research United States National Institutes of Health (U.S.)
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The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.

Journal of the American Medical Informatics Association : JAMIA Aug 13, 2021
OBJECTIVE: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.
Algorithms Biomedical Research Proteomics United States Humans Artificial Intelligence COVID-19 World Health Organization Databases as Topic National Institutes of Health (U.S.)
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Ten computer codes that transformed science.

Nature Jan 1, 2021
Algorithms Databases, Factual Deep Learning Time Factors Neural Networks, Computer National Library of Medicine (U.S.) History, 20th Century Databases, Nucleic Acid National Institutes of Health (U.S.) Preprints as Topic Fourier Analysis Physics Weather Computational Biology Programming Languages United States Databases, Protein Datasets as Topic History, 21st Century
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Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.

In vivo (Athens, Greece) Jun 1, 2020
BACKGROUND/AIM: To evaluate the research trends in coronavirus disease (COVID-19).
Coronavirus Infections Information Dissemination Databases, Bibliographic National Institutes of Health (U.S.) United States Bibliometrics Publishing Pneumonia, Viral Research Support as Topic Humans Machine Learning Research China Pandemics COVID-19
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Big Data and Radiology Research.

Journal of the American College of Radiology : JACR Sep 1, 2019
Our understanding of human health may be significantly enhanced in the near future because of the unprecedented volume of digitized health care data and the availability of artificial intelligence to mine these data for correlations that could drive ...
National Institutes of Health (U.S.) United States Radiology Humans Quality Improvement Algorithms Female Forecasting Male Artificial Intelligence Big Data Machine Learning
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