Results 191 to 200 of about 2,522,226 (288)

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

Predicting Post-Induction Hypotension in Diverse Surgical Populations: A Multiclass Classification Universal Model Using Machine Learning Techniques. [PDF]

Yonsei Med J
Lee SW, Lee D, Kim SH.
europepmc   +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

Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study. [PDF]

JMIR Med Inform
Wang HJ, Lee WP, Gau TP, Cheng KI, Wei CR.   +4 more
europepmc   +1 more source

Extending Service Beyond Active Duty: Generalizability of Rheumatoid Arthritis Research Conducted Among US Veterans

Arthritis Care &Research, EarlyView.
Biomedical research involving US veterans continues to advance health care 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 veteran ...
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

A Folded, Structure-Integrated Bimodal Sensor Enabling Non-Contact and Tactile Perception for Intelligent Robots. [PDF]

Adv Sci (Weinh)
Yang W, Guo Y, Han M, Feng B, Ma Y, Xu B, Liu F, Wang D, Hao P, Ding X, Luo S.   +10 more
europepmc   +1 more source

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

Uncovering core regulators of multi-abiotic stress adaptation in Arabidopsis thaliana through integrative meta-analysis and machine learning with RT-qPCR validation. [PDF]

Sci Rep
Mehdizadeh Hakkak M, Tohidfar M.
europepmc   +1 more source

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

Transcriptomics- and machine learning-based identification and mechanistic <i>in vitro</i> validation of a cardiovascular risk gene signature at the CKD-cancer intersection: a multi-omics approach. [PDF]

Front Cardiovasc Med
Lin C, Shao S, Xia M, Lu S.
europepmc   +1 more source