Results 171 to 180 of about 71,766 (218)

Supervising Trainee Inter-Visit Care Using EHR Learning Analytics: Formative Training Tool or Threat to Well-Being? [PDF]

J Grad Med Educ
Boggs Z, Frazier H, Kon R.
europepmc   +1 more source

Generative Artificial Intelligence Utilization and Perspectives Among Reproductive‐Age Women With Systemic Autoimmune and Rheumatic Diseases

Arthritis Care &Research, EarlyView.
Objective Reproductive‐age women with systemic autoimmune and rheumatic diseases (SARDs) have unique information needs related to their SARDs and reproductive health. We sought to understand their use of and receptivity to current and hypothetical generative artificial intelligence (AI) tools for health information‐seeking. Methods We conducted a cross‐
Mariam Arif, Catherine Wright, Samantha Rodriguez, Sarah Graves, Tamar Krishnamurti, Mehret Birru Talabi   +5 more
wiley   +1 more source

More, better feedback please: are learning analytics dashboards (LAD) the solution to a wicked problem? [PDF]

Adv Health Sci Educ Theory Pract
Kitto S, Chiang HLM, Ng O, Cleland J.
europepmc   +1 more source

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

Using Learning Analytics to Evaluate the Clinical Education Podcast Format. [PDF]

Med Sci Educ
Horta L, Ho D, Lau KHV.
europepmc   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial

Arthritis Care &Research, Accepted Article.
Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Utilisation of Learning Analytics to Identify Students at Risk of Poor Academic Performance in Medical Schools. [PDF]

Cureus
Wong TL, Hope D, Jaap A.
europepmc   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

A review of learning analytics opportunities and challenges for K-12 education. [PDF]

Heliyon
Paolucci C, Vancini S, Bex Ii RT, Cavanaugh C, Salama C, de Araujo Z.   +5 more
europepmc   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source