Results 161 to 170 of about 44,463 (307)

Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension

Risk Management and Healthcare Policy
Jiaxin Zan,1,2 Xiaojing Dong,1,2 Hong Yang,1,2 Jingjing Yan,1,2 Zixuan He,3 Jing Tian,3 Yanbo Zhang1,2,4 1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China; 2Shanxi Provincial Key ...
Zan J, Dong X, Yang H, Yan J, He Z, Tian J, Zhang Y   +6 more
doaj  

Shap Abbey, Westmorland

'SHAP ABBEY. Westmoreland. J. Stubbs del et litho. 1837.
Stubbs, J.
core  

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

Advanced Intelligent Systems, EarlyView.
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna, Hugo A. Ordoñez Erazo, Carlos A. Cobos Lozada   +2 more
wiley   +1 more source

A Global Prospective Harmonization Framework for Suicidality, Anhedonia, and Obsessive‐Compulsive Symptoms in Psychiatric Genetic Studies: A Cross‐Continental Study Within the Ancestral Population Network

American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, EarlyView.
ABSTRACT This study aims to prospectively collect harmonized, quantitative, and dimensional psychiatric phenotypes (suicidality, anhedonia, and obsessive‐compulsive symptoms) and information on discrimination, stigma, and unfair treatment in up to 27,500 individuals across diverse ancestries and clinical populations for genetic analysis within the NIMH
Ana M. Diaz‐Zuluaga, Jacey L. Anderberg, Ana M. Ramirez‐Diaz, Andrea Horvath Marques, Catherine E. Rast, Daniel Bustamante, Deborah Jonker, Johanna Valencia‐Echeverry, Joseph Kyebuzibwa, Josselyn S. Muñoz, Kristien van der Walt, Mauricio Castaño Ramirez, Olivia Wootton, Renee M. Frederick, Rocky E. Stroud II, Ruben Gur, Sang Jin Rhee, Shaili C. Jha, Stella Gichuru, Susan Service, Victor I. Reus, Akena Dickens, Carlos Lopez‐Jaramillo, Carrie E. Bearden, Dan J. Stein, Ezra S. Susser, James J. Crowley, Jonathan Flint, Kenneth S. Kendler, Lukoye Atwoli, Michele T. Pato, Nelson B. Freimer, Roel A. Ophoff, Yong Min Ahn, Loes Olde Loohuis, Eric A. Storch, Bizu Gelaye   +36 more
wiley   +1 more source

Use of Machine Learning to Identify Markers of Risk for Fragile X‐Associated Tremor/Ataxia Syndrome: A Preliminary Analysis

Annals of Neurology, EarlyView.
Objective The objective of this study was to examine whether machine learning has the capacity to prospectively identify and predict the emergence of Fragile X‐associated tremor/ataxia syndrome (FXTAS) among male fragile X premutation carriers (PCs). Methods We explored neuropsychological and motor evaluation metrics, brain magnetic resonance imaging ...
Chitrabhanu Gupta, Angeela Poudel, Jun Yi Wang, Randi Hagerman, Glenda Espinal, Jessica Famula, Andrea Schneider, Flora Tassone, Susan M. Rivera, Chen‐Nee Chuah, David Hessl   +10 more
wiley   +1 more source

Computed Tomography‐Based Morphometric Analysis Reveals Intercondylar Notch Stenosis and Osteophytes Are Associated With Anterior Cruciate Ligament Injury

Arthroscopy, EarlyView.
Purpose To quantitatively assess intercondylar notch morphometrics using 3‐dimensional computed tomography reconstruction, evaluate their association with anterior cruciate ligament (ACL) injury in Asian populations, and investigate the prevalence of osteophytes in patients with ACL injury.
Xiaozhong Ma, WenMing Jiang, Chao Lu, Tongyue Ji, Su Yan, Heng Zhu, Xue He, Hao Shu, Lei Wang, Luning Sun   +9 more
wiley   +1 more source

Identifying Systemic Lupus Erythematosus From Serum Proteomic Profiles Using Machine Learning and Genetic Risk Stratification

Arthritis &Rheumatology, EarlyView.
Objective Proteome‐wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles. Methods Patients with lupus and individuals with other autoimmune diseases in the UK Biobank were included.
Mehmet Hocaoǧlu, Jishnu Das, Amr H. Sawalha   +2 more
wiley   +1 more source

Machine Learning to Predict Remission Between 6 and 24 Months in Rheumatoid Arthritis: Insights From JAK, an International Registry Collaboration

Arthritis &Rheumatology, EarlyView.
Objective To develop, externally validate, and simplify a machine learning model to predict remission between 6 and 24 months in patients with rheumatoid arthritis (RA) initiating tumor necrosis factor inhibitors, JAK inhibitors, interleukin‐6 inhibitors, abatacept, or rituximab using data from 11 international registries in the JAK‐pot collaboration ...
Zubeyir Salis, Denis Mongin, Denis Choquette, Louis Coupal, Catalin Codreanu, Florenzo Iannone, Roberto Caporali, Tore K. Kvien, Sella Provan, Ruth Fritsch‐Stork, Dan Nordström, Nina Trokovic, Karel Pavelka, Jakub Závada, Ana Rodrigues, Ziga Rotar, Prodromos Sidiropoulos, Irini Flouri, Céline Lamacchia, Michele Iudici, Delphine Courvoisier, Kim Lauper, Axel Finckh   +22 more
wiley   +1 more source

Inferring Rheumatoid Arthritis Disease Activity Status From the Electronic Health Records Across Health Systems

Arthritis &Rheumatology, EarlyView.
Objective Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. The inconsistent availability of data on disease activity in real‐world electronic health records (EHRs) data has limited the ability to generate real‐world evidence (RWE).
David Cheng, Xuan Wang, Gregory C. McDermott, Jennifer S. Hanberg, Zoe Love, Katherine Zhong, Mary Jeffway, Jue Hou, Vidul Panickan, Rahul Sangar, Ying Qi, Connor Melley, Lauren Costa, Dakota Feil, Rachael Matty, Dana Weisenfeld, Abisayo Animashaun, Aimee Schreiner, Sara Morini, Lauren Rusnak, Andrew Cagan, Misti Paudel, J. Michael Gaziano, Brian Sauer, Michael Weinblatt, Joshua Baker, Bryant England, Yuk‐Lam Ho, Kelly Cho, Paul Monach, Grant W. Cannon, Nancy Shadick, Ted R. Mikuls, Tianxi Cai, Katherine P. Liao   +34 more
wiley   +1 more source

Applying machine learning to pharmacovigilance data: A proof‐of‐concept study

British Journal of Clinical Pharmacology, EarlyView.
Aim Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance database (FNPV), this proof‐of‐concept study aimed to assess the feasibility of using a ML algorithm—eXtreme Gradient Boosting (XGBoost)—combined with SHapley Additive exPlanations (SHAP) analysis, to ...
Romain Barus, Pauline Schiro, Jean‐Luc Faillie, Romane Freppel, Johana Béné, Claire de Canecaude, François Montastruc   +6 more
wiley   +1 more source