Results 171 to 180 of about 85,695 (269)

Machine‐Learning‐Enabled Wood with Nanopump Functionalization for Solar Interfacial Evaporation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed machine learning to design an iron‐cobalt‐carbon‐wood photothermal material, achieving high‐efficiency evaporation at 2.807 kg m−2 h−1 and excellent salt resistance. The integrated system increased the daily water production efficiency of solar distillation by 1.5 times, providing an innovative solution for sustainable seawater ...
Chaohai Wang, Rui Li, Chang He, Xinfeng Zhu, Junning Wang, Rongfu Peng, Ho Ngoc Nam, Yanli Mao, Wei Liu, Mingshan Zhu, Yusuke Yamauchi   +10 more
wiley   +1 more source

Interpretable AI for treatment decision-making in immunoradiotherapy of locally advanced nasopharyngeal carcinoma. [PDF]

Front Oncol
Cao G, Zeng B, Yuan Z, Hu X, Ou H.
europepmc   +1 more source

Machine learning‐based prediction of elevated N terminal pro brain natriuretic peptide among US general population

ESC Heart Failure, Volume 12, Issue 2, Page 859-868, April 2025.
Abstract Aims Natriuretic peptide‐based pre‐heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre‐heart failure, has not been well established.
Yuichiro Mori, Shingo Fukuma, Kyohei Yamaji, Atsushi Mizuno, Naoki Kondo, Kosuke Inoue   +5 more
wiley   +1 more source

Explainable Artificial Intelligence Unravels the Possible Distinct Roles of VKORC1 and CYP2C9 in Predicting Warfarin Anticoagulation Control. [PDF]

Med Sci (Basel)
Sridharan K, Sivaramakrishnan G.
europepmc   +1 more source

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography

Epilepsia, EarlyView.
Abstract Objective This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs).
Émile Lemoine, An Qi Xu, Mezen Jemel, Frédéric Lesage, Dang K. Nguyen, Elie Bou Assi   +5 more
wiley   +1 more source

Deep learning for incidence rate prediction and radiation risk assessment of solid tumors. [PDF]

Sci Rep
Liu Z, Nakamizo T, Misumi M, Ono S, Shuryak I, Ullrich RL.   +5 more
europepmc   +1 more source

Spectral entropy variability of intraoperative electrocorticography predicts outcome after epilepsy surgery in people with focal cortical dysplasia

Epilepsia, EarlyView.
Abstract Objective Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is difficult to predict. We explored whether spectral entropy, a fast computable electroencephalographic (EEG) feature, could estimate epileptic activity in intraoperative electrocorticography (ioECoG) and
Eline V. Schaft, Sem Hoogteijling, Dongqing Sun, Cyrille H. Ferrier, Pieter van Eijsden, Wim M. Otte, Galia Anguelova, Nick F. Ramsey, Maryse A. van't Klooster, Maeike Zijlmans, on behalf of the RESPect database study group, Charlotte van Asch, Lydia van de Berg, Dorien van Blooijs, Kim Boshuisen, Mireille Bourez, Kees Braun, Jan Willem Dankbaar, Matteo Demuru, Pieter van Eijsden, Cyrille Ferrier, Tineke Gebbink, Fernando Gomez‐Acebo Ruiz, Peter Gosselaar, Kimberley Grasmeijer, Renate van Griethuysen, Jiaojiao Guo, Irene Heijink, Monique Hobbelink, Friso Hoefnagels, Sem Hoogteijling, Geertjan Huiskamp, Sarah Jacobs, Floor Jansen, Susanne Jelsma, Nicole van Klink, Merel Koopmans, Frans Leijten, Wouter Maathuis, Mariska Mantione, Angelika Muhlebner, Brechje Olgers, Janine van Ophorst, Miranda Postma, Sandra van der Salm, Eline Schaft, Anniek van Schaik, Monique van Schooneveld, Dongqing Sun, Ziyi Wang, Martine van Zandvoort, Eva Zuidhoek, Willemiek Zweiphenning, Jack Zwemmer   +53 more
wiley   +1 more source

Integrating machine learning with SHAP to uncover multi-tissue molecular signatures in Osteoarthritis progression. [PDF]

PLoS One
Zhao J, Tao J, Song Y, Yang J, Lin X, Ye Z, Lu C, Zeng M, Chen W, Liu W.   +9 more
europepmc   +1 more source

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

SHAP-explained machine-learning model for high-risk gastric cancer identification. [PDF]

Front Oncol
Oh HJ, Kim CH, Jun JK, Suh M, Choi KS, Choi IJ, Park B.   +6 more
europepmc   +1 more source