Results 171 to 180 of about 28,332 (249)

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

Machine Learning‐Guided Design of Next‐Generation Battery Materials for Sustainable Energy Storage Systems

Electron, EarlyView.
Machine learning models were developed to predict material stability and sustainability of battery compounds using data‐driven descriptors. The random forest model demonstrated superior performance, enabling efficient screening and identification of environmentally sustainable and thermodynamically stable materials for next‐generation energy storage ...
Godfrey Perfectson Oise, Felix Oshiorenoya Uloko, Immunhierokene Clinton Obrorindo, Augustine Emakpor Otorokpo   +3 more
wiley   +1 more source

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

, 2020
Cavallaro M, Moiz H, Keeling MJ, McCarthy ND.   +3 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

DFT and Machine Learning Analysis of Eco‐Friendly Formamidinium Tin Iodide Perovskite Solar Cells: Optimizing Charge Transport Layers Through SCAPS‐1D Simulations

Energy Science &Engineering, EarlyView.
Machine learning using Random Forest, GB, XGBoost, and LightGBM regression algorithms: SHAP plot for PCE. ABSTRACT Tin‐based solar cells are the eco‐friendly alternative to the toxic lead‐based cells that are currently in use. In this context, Formamidinium Tin Iodide (FASnI3) emerges as a promising candidate due to its superior performance, exhibiting
Rabeya Khan, Md Rasidul Islam, I.K. Gusral Ghosh Apurba, Md. Amimul Ihsan, Rahat Ul Nasib, Mohammad Arefin, Md Rifatul Islam, Abu Zahid, Hasnain A. Ziad, Jehan Y. Al‐Humaidi   +9 more
wiley   +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

Robustness of nutrient-based NOVA food classification under profile-separated nested validation. [PDF]

Front Nutr
Gören SS, Ali RH, Khan TA, Ahmed I.
europepmc   +1 more source

Beyond Prediction: Data, Baselines, Explanation, and Causation in Machine Learning for Food Insecurity

Food Safety and Health, EarlyView.
The gains from machine learning in nowcasting and forecasting food insecurity are still small and limited and cannot be observed in all countries. This review summarizes the public resources for data, corrects common misconceptions about model requirements, and establishes baseline requirements, explanations, causal inference, and equity in operational
Shabnam Mehboob, Azman Abdullah, Gunesh Kumar, Samreen Memon, Naila Afghan   +4 more
wiley   +1 more source

Chemical Drivers of Sweetness, Sourness, and Astringency in Apple Juice Identified Using Support Vector Machine Regression. [PDF]

J Food Sci
Berardinelli A, Cliceri D, Endrizzi I, Gasperi F, Vrhovsek U, Aprea E.   +5 more
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