Results 161 to 170 of about 24,298 (249)

Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations. [PDF]

J Thorac Dis
Wang S, Lin Y, Shi H, Liang P, Luo Z, Kong J, Huang J, Cheng M, Zhang B, Wang Y, Kan H, Liang L, Xie W.   +12 more
europepmc   +1 more source

Long‐term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data

Epilepsia, EarlyView.
Abstract Objective Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI using routine clinical data. The objective of this study was to develop and validate a machine learning approach to predict the onset of posttraumatic epilepsy (PTE) over varying time horizons ...
Mustafa Ozmen, Shashank Vadlamani, James J. Gugger, Megan Amuan, Ramon Diaz‐Arrastia, Mary Jo Pugh, Eamonn Kennedy   +6 more
wiley   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source

Inter‐Material Transfer Learning for Accelerated Nanofluid Heat Transfer Prediction: A Machine Learning Approach for Energy Systems

Energy Science &Engineering, EarlyView.
This study presents an inter‐material transfer learning framework for nanofluid heat transfer prediction in energy systems. By leveraging knowledge from Al2O3‐water data, the model accurately predicts hybrid Al2O3‐TiO2 nanofluid performance with only 20 simulations, achieving R2 = 0.985 and reducing computational requirements by 78. ABSTRACT This paper
Soumaya Hadj Salah, Aymen Ochi, Sonia Bezi   +2 more
wiley   +1 more source

Interpretable machine learning model for predicting myocardial injury in intensive care unit patients using SHapley Additive exPlanations analysis. [PDF]

Sci Prog
Liu X, Chen G, Hao C, An Y, Zhao H.
europepmc   +1 more source

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis. [PDF]

Front Med (Lausanne)
Chen B, Huan L, Lu J, Yuan J.
europepmc   +1 more source

Majorbio Cloud 2026 Update: An End‐to‐End Solution for Proteomics Research

iMetaMed, EarlyView.
The recent upgrade of Majorbio Cloud facilitates a more streamlined and accurate research process, aiding researchers across the various proteomics subfields. Furthermore, it propels the advancement of multi‐layered, multi‐omics scientific investigations centered around proteins.
Caiping Shi, Chang Han, Jichen Han, Bing Yang, Yachen Dong, Jianhua Zhao, Binxu Liu, Rundong Fang, Wenyao Fu, Jiawen Ma, Meng Luo, Lixing Feng, Yitong Feng, Huimin Cui, Mengxi Li, Huasheng Huang, Xianglin Zhang, Kegang Yu   +17 more
wiley   +1 more source

Machine Learning and Shapley Additive Explanations Value Integration for Predicting the Prognostic of Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Model Development and Evaluation Study. [PDF]

JMIR Med Inform
Wang J, Wu H, Cai H, Wang Y, Gu J.
europepmc   +1 more source

Decomposition of Shapley additive explanations for a stacking model

Communications for Statistical Applications and Methods
openaire   +1 more source