Results 191 to 200 of about 29,456 (261)

The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach. [PDF]

Front Neurol
Chen A, Wang J, Huang Q, Shen C, Hu Z, Hu X.   +5 more
europepmc   +1 more source

Toward a Paradigm Shift in Low‐Temperature SCR Catalyst Design: Defect Engineering and Data‐Driven Integration

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This work systematically reviews the key factors influencing the performance of low‐temperature NH3‐SCR. The mechanism and challenges of defect engineering strategies, such as oxygen vacancies, heteroatom doping, crystal facet exposure, and surface reconstruction, in controlling both activity and selectivity were analyzed.
Rongrong Kan, Pinjing He, Fan Lü, Hua Zhang   +3 more
wiley   +1 more source

Positive Association Between the Albumin-Bilirubin (ALBI) Score and Septic Shock in Patients with <i>Escherichia coli</i> Bloodstream Infection: A Retrospective Cohort Study. [PDF]

Infect Drug Resist
Wang Y, Huang Y, Hu M, Zhen P.
europepmc   +1 more source

Electronic Structure Modulation in Dopant‐Controlled Single‐Atom Graphene Catalysts for Efficient Hydrogen Evolution: A Machine Learning and First‐Principles Study

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
First‐principles DFT calculations and machine learning analysis show that heteroatom doping of graphene (G) significantly enhances the stabilization of transition‐metal single atoms by strengthening metal–support interactions and increasing charge transfer. N‐doped G exhibits higher adsorption energies, lower d‐band centers, and shorter TM–G bonds than
Sajjad Ali, Pir Muhammad Ismail, Muhammad Humayun, Mohamed Bououdina   +3 more
wiley   +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

Interpretable Machine Learning Models Based on Blood Cell-Derived Inflammatory Indices for Identifying Colorectal Neoplasia: A Retrospective Study. [PDF]

J Inflamm Res
Zeng X, Li Z, Cai L, Liang S, Wang S, Li K, Chen N, Yang X, Liu S.   +8 more
europepmc   +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

Development and Validation of a Machine Learning-Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study. [PDF]

JMIR Med Inform
Ding W, Fang L, Fang C.
europepmc   +1 more source