Results 121 to 130 of about 68,798 (306)

Extending Service Beyond Active Duty: Generalizability of Rheumatoid Arthritis Research Conducted Among U.S. Veterans

Arthritis Care &Research, Accepted Article.
Biomedical research involving United States Veterans continues to advance healthcare beyond the Veterans Health Administration. This is particularly true in rheumatoid arthritis (RA), where Veteran‐centric research has uncovered novel insights into pathogenesis, risk factors, and disease manifestations, informing clinical care and research across both ...
Austin M. Wheeler, Joshua F. Baker, Jennifer L. Barton, Maria I. Danila, Tate M. Johnson, Kamini Kuchinad, Kaleb Michaud, Amir A. Razmjou, J. Steuart Richards, Namrata Singh, Isaac D. Smith, Katherine D. Wysham, Grant W. Cannon, Bryant R. England, Amy Joseph, Deana Lazaro, Katherine P. Liao, Courtney Loecker, Andreas Reimold, Beth I. Wallace, Ted R. Mikuls   +20 more
wiley   +1 more source

Predicting Disease Activity Score in Rheumatoid Arthritis Patients Treated with Biologic Disease-Modifying Antirheumatic Drugs Using Machine Learning Models

Technologies
Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint inflammation and progressive disability. While biological disease-modifying antirheumatic drugs (bDMARDs) have significantly improved disease control, predicting individual ...
Fatemeh Salehi, Sara Zarifi, Sara Bayat, Mahdis Habibpour, Amirreza Asemanrafat, Arnd Kleyer, Georg Schett, Ruth Fritsch-Stork, Bjoern M. Eskofier   +8 more
doaj   +1 more source

Anomaly analytics in data-driven machine learning applications

International Journal of Data Science and Analytics
Abstract Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not only on the model creation process, but also on the input data quality. We investigate here the impact of data quality on the quality of the ML model in a generic way.
Shelernaz Azimi, Claus Pahl
openaire   +2 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Multimedia and machine learning approaches for data analytics [PDF]

Multimedia Tools and Applications, 2020
openaire   +2 more sources

Predictive Analytics for Dementia: Machine Learning on Healthcare Data

CoRR
Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer's disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic ...
Shafiul Ajam Opee, Nafiz Fahad, Anik Sen, Rasel Ahmed, Fariha Jahan, Md. Kishor Morol, Md. Rashedul Islam   +6 more
openaire   +2 more sources

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Machine Learning in Science and Engineering Conference - Machine Learning and Event Detection for Population Health

, 2019
Presented on June 12, 2019 at 9:00 a.m. in the Georgia Tech Hotel and Conference Center, Georgia Institute of Technology.The second-annual Machine Learning in Science and Engineering (MLSE) Conference highlights advances in research that utilize methods ...
Neill, Daniel B.
core   +1 more source

Enhancing Governmental Decision-Making through Predictive Analytics with Machine Learning-Based Data-Driven Framework [PDF]

Government bodies around the world are going digital and slowly starting to make use of data driven technologies to make better, faster and more transparent decisions.
Al-Inizi, Mahdi Salah Mahdi
core   +1 more source