Results 141 to 150 of about 36,209 (267)

Efficacy and safety of Oliceridine versus Sufentanil in postoperative analgesia for burn skin grafting: a machine learning and SHAP-based cohort study. [PDF]

Front Med (Lausanne)
Tian Y, Zhang Y, Feng D, Wang H, Ma Z.
europepmc   +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

Understanding Transformer-Based Classifications of Medical Text Using a Large Language Model for the Attribution of Feature Importance: Proof-of-Concept Algorithm Development and Validation Study. [PDF]

JMIR Med Inform
Zhou F, Saha A, Afzal M, Parrish R, Haynes RB, Iorio A, Lokker C.   +6 more
europepmc   +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

Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning. [PDF]

PLoS One
Lee EJ, Kim S, Lee HJ, Jhong JH.
europepmc   +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

Machine Learning-Based Radiopatho-Clinical Model Integrating Ultrasound Radiomics and Kleiner Score for Prognosis Prediction in NAFLD-Related Hepatocellular Carcinoma. [PDF]

J Hepatocell Carcinoma
Li CL, Jia Z, Yao ZY, Cui XT, Sun A, Hao AY, Chen ZY, Chen F, Cao JY, Wang ZS.   +9 more
europepmc   +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

Explainable time-series forecasting with sampling-free SHAP for Transformers. [PDF]

Nat Commun
Hertel M, Pütz S, Mikut R, Hagenmeyer V, Schäfer B.   +4 more
europepmc   +1 more source

Do Governance Structures Drive Green Building Adoption? A Machine Learning Approach With Random Forests

Business Strategy and the Environment, EarlyView.
ABSTRACT This study examines the determinants of firms' propensity to adopt green buildings in the Euro Stoxx 300 and the S&P 500 indices, during 2012–2023. Using random forest binary classifiers, we assess the relative importance of financial, sectoral, geographic, and climate governance predictors and uncover nonlinear relationships often overlooked ...
María del Carmen Valls Martínez, José Manuel Santos‐Jaén, María Estefanía Sánchez Pacheco, Fernando José Zambrano Farías   +3 more
wiley   +1 more source