Results 161 to 170 of about 53,216 (300)

Intelligent diagnosis‐related group classification for 134,189 patients in eastern China using machine learning

Journal of Intelligent Medicine, EarlyView.
Intelligent DRG Classification with ELGWO‐LightGBM. Abstract Diagnosis‐related group (DRG) classification is crucial for healthcare cost management and resource allocation, but traditional manual classification by physicians is inefficient and error‐prone, especially for large‐scale medical data.
Yanxi Zhang, Yihan Cheng, Yuhui Ruan, Kun Wang   +3 more
wiley   +1 more source

SparGlu: A sparse‐data evaluation framework for gated recurrent unit‐based blood glucose prediction across varying monitoring intervals

Journal of Intelligent Medicine, EarlyView.
A sparse‐aware GRU model accurately predicts blood glucose levels using downsampled and limited data, achieving clinically safe forecasts even under low‐monitoring conditions. By effectively capturing glucose dynamics through residual learning, the model supports personalized diabetes self‐management, offering a practical solution for resource‐limited ...
Genet Tadese Aboye, Jasper Gielen, Gizeaddis Lamesgin Simegn, Jean‐Marie Aerts   +3 more
wiley   +1 more source

Advanced RNN based NARMA predictors

, 2000
An analysis of nonlinear time series prediction schemes, realised though advanced Recurrent Neural Network (RNN) techniques is provided. Due to practical constraints in using common RNNs, such as the problem of vanishing gradient, some other ways to ...
Chambers, JA, Mandic, DP
core  

Short‐Term Agricultural Drought Prediction Using Explainable Data‐Driven Models and Quantified Uncertainty

International Journal of Climatology, EarlyView.
This study demonstrates that explainable deep learning models can skilfully predict short‐term agricultural drought using standardized drought indices, capturing hydroclimatic memory and seasonal persistence. The integration of uncertainty quantification and SHAP‐based interpretability reveals the dominant role of antecedent moisture and climate ...
K. Saranya Das, N. R. Chithra, Venkataramana Sridhar   +2 more
wiley   +1 more source

TEAM: A time‐enhanced attention‐based model for virus mutation prediction [PDF]

Quant Biol
Abstract The evolution of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) during the coronavirus disease 2019 (COVID‐19) pandemic highlights the critical need for predicting virus mutations in order to stay ahead of infectious diseases. Here, we present the time‐enhanced attention‐based model (TEAM), which combines phylogenetic sampling ...
Ji J, Hu J, Yu T, Fan X.
europepmc   +2 more sources

Artificial Intelligence‐Driven Natural Product Drug Discovery: From Computational Genome Mining to Clinical Translation

Medicinal Research Reviews, EarlyView.
ABSTRACT Natural products (NPs) have historically yielded numerous therapeutic agents, yet their integration into modern drug discovery has been constrained by chemical complexity, low abundance, laborious dereplication, and limited target annotation.
Antonio Lavecchia
wiley   +1 more source

Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex. [PDF]

Nat Neurosci
Bowler JC, Azhar DB, Jensen CM, Lee HW, Heys JG.   +4 more
europepmc   +1 more source

An Integrated Statistical and Machine Learning Framework for Predicting the Dry Sliding Tribological Behavior of CFRP and GFRP Composites Under Environmental Aging and Loading Conditions

Polymer Composites, EarlyView.
The graphical abstract presents the experimental and data‐driven framework used to investigate the dry sliding tribological behavior of CFRP and GFRP composites. The effects of seawater, engine oil, diesel fuel, aging time, and normal load on the specific wear rate (SWR) and coefficient of friction (COF) were evaluated using pin‐on‐disc tests, ANOVA ...
Ahmet Saylık, İhsan Tuğal, Şemsettin Temiz   +2 more
wiley   +1 more source

Artificial intelligence, equity, and pediatric neurodevelopmental disorders: A scoping review of clinical practice applications

Pediatric Investigation, EarlyView.
Artificial intelligence (AI) is being explored to support diagnosis and care for pediatric neurodevelopmental disorders, yet most tools remain in early stages of development. This scoping review identifies limited external validation, narrow population representation, and sparse equity considerations, underscoring the need for inclusive, clinically ...
Florida Uzoaru, Obinna O. Oleribe, Ucheoma Nwaozuru, Parichart Sabado   +3 more
wiley   +1 more source

Protocol for analyzing slow cortical dynamics in mouse neuronal recordings. [PDF]

STAR Protoc
Shymkiv Y, Yuste R.
europepmc   +1 more source