Results 81 to 90 of about 67,619 (257)

CSF Cytokine Network Organization Predicts Progression Independent of Relapse and MRI Activity in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Progression independent of relapse activity is a major determinant of long‐term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse‐independent progression in radiologically stable relapsing–remitting multiple sclerosis is associated with distinct ...
Antonio Bruno, Matteo Conti, Ettore Dolcetti, Federica Azzolini, Angela Borrelli, Roberta Fantozzi, Mario Stampanoni Bassi, Giovanni Galifi, Giuseppe Maccarrone, Luca Montaguti, Marco Cervigni, Maddalena Dal Pozzo, Roberto Furlan, Annamaria Finardi, Girolama Alessandra Marfia, Marco Salvetti, Antonella Conte, Fabio Buttari, Diego Centonze, Luana Gilio   +19 more
wiley   +1 more source

Early Clinical and Cerebrospinal Fluid Predictors of 1‐Year Recurrence in Autoimmune GFAP Astrocytopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Autoimmune glial fibrillary acidic protein astrocytopathy (GFAP‐A) is an inflammatory central nervous system disorder with variable outcomes. Relapse occurs in a subset of patients, but early predictors remain unclear. We aimed to identify admission‐available features associated with 1‐year recurrence and develop an interpretable ...
Qingting Hong, Yuping Xiao, Yuhan Wu, Ziyuan Huang, Weishan Liang, Li Xiao, Yaqing Shu, Tingting Lu, Yanyu Chang, Wei Qiu, Yuge Wang   +10 more
wiley   +1 more source

Interpretable Machine Learning for Survival Analysis

Biometrical Journal
ABSTRACT With the spread and rapid advancement of black box machine learning (ML) models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade.
Sophie Hanna Langbein, Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek, Marvin N. Wright   +5 more
openaire   +5 more sources

Model-Agnostic Interpretability of Machine Learning

CoRR, 2016
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces.
Marco Túlio Ribeiro, Sameer Singh 0001, Carlos Guestrin   +2 more
openaire   +2 more sources

Interpretable machine learning for CMIP6 multi-model ensembles

Environmental Data Science
Multi-model ensembles are widely used in climate science, yet Coupled Model Intercomparison Project Phase 6 (CMIP6) models are neither independent nor equally skillful.
Siyi Wu, Steve M. Easterbrook
doaj   +1 more source

Anticoagulation Treatment Response Among Phenotypic ESUS Clusters: Evidence From CASPR

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To evaluate treatment effect heterogeneity across phenotypic clusters of patients with cryptogenic embolic stroke using causal inference methods. Methods From a retrospective, multicenter cohort of patients with cryptogenic embolic stroke, we estimated the treatment effect of anticoagulation versus antiplatelet therapy in patients ...
Elena Badillo Goicoechea, Patrick Ellsworth, James E. Siegler, James R. Brorson, Christina M. Lineback, Frank G. Conyers, Deborah Kerrigan, Kelsey Eklund, Shadi Yaghi, Farid Khasiyev, Emiliya Melkumova, Collin J. Culbertson, Marissa D'Souza, Dinesh V. Jillella, Patrick Glover, Hassan Aboul‐Nour, Fadi Nahab, Mahan Shahrivari, Simona Nedelcu, David S. Liebeskind, Matthew M. Smith, Aaron Rothstein, Anna Bowman, Alicia Zha, Narendra Kala, Ahmad Abu Qdais, Sami Al Kasab, Mudassir Farooqui, Diana Rojas‐Soto, Yasmin Aziz, Franziska Herpich, Jean‐Philippe Daniel, Sean L. Thompson, Vivek Patel, Thanh N. Nguyen, Siddharth Sehgal, Wenzheng Yu, Wayneho Kam, Russel Van Coevering, Oana M. Dumitrascu, Muhammad Alvi, Balaji Krishnaiah, Setareh Salehi Omran, Guillermo Linares, Eesha Singh, Hamid Ali, Mary Penckofer, Jesse M. Thon, Shani Mannion, Dalia Chahien, Lucia Chen, Fawad Talat, Mehdi Abbasi, Richa Sharma   +53 more
wiley   +1 more source

Interpretable machine learning for precision cognitive aging

Frontiers in Computational Neuroscience
IntroductionMachine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare.
Abdoul Jalil Djiberou Mahamadou, Emma A. Rodrigues, Vasily Vakorin, Vasily Vakorin, Violaine Antoine, Sylvain Moreno, Sylvain Moreno   +6 more
doaj   +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

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

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