AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

National Institutes of Health (U.S.)

Showing 11 to 19 of 19 articles

Clear Filters

Big Data and Radiology Research.

Journal of the American College of Radiology : JACR
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 ...

A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.

Osteoarthritis and cartilage
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...

A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.

International journal of medical informatics
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).

Machine learning reveals chronic graft--host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.

Haematologica
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...

A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research.

American journal of preventive medicine
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 ...

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

Journal of biomedical semantics
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...

ProvCaRe Semantic Provenance Knowledgebase: Evaluating Scientific Reproducibility of Research Studies.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

KnowEnG: a knowledge engine for genomics.

Journal of the American Medical Informatics Association : JAMIA
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...