Results 161 to 170 of about 13,423,347 (294)

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach. [PDF]

Genes (Basel), 2023
Soriano B, Hafez AI, Naya-Català F, Moroni F, Moldovan RA, Toxqui-Rodríguez S, Piazzon MC, Arnau V, Llorens C, Pérez-Sánchez J.   +9 more
europepmc   +1 more source

The Association of Physical Function With Psychosocial Patient‐Reported Outcomes in People With Systemic Lupus Erythematosus

Arthritis Care &Research, EarlyView.
Objective To evaluate how modifiable psychosocial factors and fatigue relate to physical functioning in patients with systemic lupus erythematosus (SLE). Methods In this cross‐sectional study of two demographically distinct cohorts (Approaches to Positive, Patient‐Centered Experiences of Aging with Lupus [APPEAL] and California Lupus Epidemiology Study
Mrinalini Dey, Jessica Fitzpatrick, Maria Dall'Era, Charmayne Dunlop‐Thomas, Courtney Hoge, S. Sam Lim, Patricia P. Katz, Jinoos Yazdany, Laura C. Plantinga   +8 more
wiley   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Causal Structure Learning: A Combinatorial Perspective. [PDF]

Found Comut Math, 2022
Squires C, Uhler C.
europepmc   +1 more source

The Lupus Damage Index Revision Program: Results From the Item Generation and Reduction Phases

Arthritis Care &Research, EarlyView.
Objective A data‐driven and expert/patient consensus‐based project to develop a revised Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology (ACR) Damage Index (SDI) is under way supported by SLICC, ACR, and the Lupus Foundation of America. Our objective is to report the item generation and reduction phase results
Burak Kundakci, Megan R. W. Barber, Ann E. Clarke, Sindhu R. Johnson, Anselm Mak, Hermine I. Brunner, Murat Inanc, Rosalind Ramsey‐Goldman, Guillermo Ruiz‐Irastorza, Ellen M. Ginzler, John G. Hanly, Alexandra Legge, Cynthia Aranow, Laurent Arnaud, Nathalie Costedoat‐Chalumeau, Dafna D. Gladman, Søren Jacobsen, Michelle Petri, Clovis A. Silva, Évelyne Vinet, Alexandre E. Voskuyl, SDI Revision Literature Review Groups, SDI Revision Delphi Panel, SDI Revision Item Reduction Committee, SDI Revision Clinical Domain Groups, Ian N. Bruce   +25 more
wiley   +1 more source

Bayesian Coherence Analysis for Microcircuit Structure Learning. [PDF]

Neuroinformatics, 2023
Chen R.
europepmc   +1 more source

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, EarlyView.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source

Correction to: Bayesian Coherence Analysis for Microcircuit Structure Learning. [PDF]

Neuroinformatics, 2023
Chen R.
europepmc   +1 more source

Growing Imaginations

Details on a list of Learning About Learning children's workshops offered at Southwest Craft ...
Learning About Learning
core   +1 more source

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source