Results 161 to 170 of about 3,031,483 (337)

Metabolic Consequences of Rheumatoid Arthritis

Arthritis Care &Research, EarlyView.
Patients with rheumatoid arthritis (RA) may have metabolic disruption, which can contribute to adverse long‐term outcomes, for multiple reasons. Patients with RA appear to have a higher risk of sarcopenia, type 1 and type 2 diabetes mellitus, metabolic syndrome, and hypertension. Systemic inflammation in RA can cause a “lipid paradox,” with reduced low‐
Stevie Barry, Emily Sheng, Joshua F. Baker   +2 more
wiley   +1 more source

Implementation of Evolutionary Computation to Parallel Computers

, 2000
Mitsunori Miki
openalex   +2 more sources

Hydroxychloroquine Associated with Lower Glomerular Filtration Rate Decline in Lupus Nephritis

Arthritis Care &Research, Accepted Article.
Background Hydroxychloroquine (HCQ) protects kidney function in lupus nephritis (LN) by preventing flares, yet some cohort studies show no significant benefit in kidney function with HCQ. Clarifying these conflicting findings by showing early and long‐term benefits of HCQ on kidney function preservation is critical. Therefore, we analyzed data from our
Shivani Garg, Brad Rovin, Brad C. Astor, Tripti Singh, Fauzia Hollnagel, Megan Kuik, Lexie Kolton, Callie Saric, S. Sam Lim, Christie M. Bartels   +9 more
wiley   +1 more source

Erratum to Theme Issue on “Evolutionary Computation in Medicine”

, 2000

openalex   +1 more source

Advancing the human experience with interactive evolutionary computation [PDF]

, 2002
Hideyuki Takagi
openalex   +1 more source

Evolutionary computation: the next major transition of artificial intelligence? [PDF]

BioData Min, 2017
Sipper M, Olson RS, Moore JH.
europepmc   +1 more source

Evolutionary Computation

, 2007
Leguizamón, Guillermo, Blum, Christian, Alba, E.   +2 more
openaire   +2 more sources

An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes. [PDF]

J Healthc Eng, 2018
Barnes S, Saria S, Levin S.
europepmc   +1 more source

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

A computational laboratory for evolutionary trade networks

, 2001
David McFadzean, Deron Stewart, Leigh Tesfatsion   +2 more
openalex   +2 more sources