Results 181 to 190 of about 36,209 (267)

Development and validation of an interpretable machine learning model for predicting central lymph node metastasis in papillary thyroid cancer. [PDF]

Front Oncol
Zhou L, Lu WP, Zhang HL, Wang M, Zhang HM, Yao D, Zhao SQ.   +6 more
europepmc   +1 more source

A Review of Artificial Intelligence in Ophthalmology: Key Aspects, Challenges, and Future Directions

Eye &ENT Research, EarlyView.
ABSTRACT Artificial intelligence (AI) is increasingly reshaping ophthalmology because the specialty depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows. This review provides a clinically grounded and translationally oriented synthesis of AI in ophthalmology, covering methodological foundations ...
Partha Pratim Ray
wiley   +1 more source

Sex-Specific Biological Predictors for Identifying Individuals With Low Bone Mineral Density Using the Taiwan Biobank Database. [PDF]

J Osteoporos
Chen YH, Hsu YP, Cheng MT, Chen SK, Guo JW.   +4 more
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

Prediction of Ascending Aortic Dilation and Analysis of Influencing Factors in Bicuspid Aortic Valve Patients Using an Explainable Machine Learning Model. [PDF]

Cardiol Res Pract
He S, Yan N, Nie G, Wang F, Yang X, Wang L, Na L.   +6 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

Metabolic characteristics and factors associated with prediabetes in Chinese adults based on real-world health examination data: a cross-sectional study. [PDF]

Front Nutr
Sun X, Zhou J, Chen Z, Xin Z, Ren J, Zheng Y, Wang J, Zhang J, Zhang Y, Liu L, Li B, Wang Y.   +11 more
europepmc   +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

Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning. [PDF]

iScience
Qian M, Li L, Cheng Y, Hou Y, Fu S, Xu Z.   +5 more
europepmc   +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