Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure.
Sign Up to like & getrecommendations! Published in 2022 at "JAMA cardiology"
DOI: 10.1001/jamacardio.2022.1900
Abstract: Importance Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH). Objective To develop and validate novel machine learning… read more here.
Keywords: black patients; race; non black; predicting hospital ... See more keywords
Diagnostic Accuracy of Artificial Intelligence Models for Predicting Postoperative Complications Following Free Flap Reconstruction: A Systematic Review and Meta-Analysis.
Sign Up to like & getrecommendations! Published in 2025 at "Microsurgery"
DOI: 10.1002/micr.70143
Abstract: To systematically evaluate the diagnostic performance of artificial intelligence (AI) models in predicting postoperative complications following flap surgery, and to compare the efficacy of different input modalities used in model training. read more here.
Keywords: predicting postoperative; postoperative complications; artificial intelligence; models predicting ... See more keywords
In vitro Models for Predicting Bioadhesion Fracture Strength to Ex Vivo Animal Buccal Tissue.
Sign Up to like & getrecommendations! Published in 2024 at "Small"
DOI: 10.1002/smll.202310363
Abstract: Commitment to the 3Rs principle (Replacement, Reduction, and Refinement) led to the development of a cell-based system to measure buccal bioadhesion in vitro and replace the use of porcine buccal and esophageal tissues (PBT and… read more here.
Keywords: models predicting; pbt pet; bioadhesion; vitro models ... See more keywords
Risk models for predicting the health-related quality of life of caregivers of youth with gastrointestinal concerns
Sign Up to like & getrecommendations! Published in 2020 at "Quality of Life Research"
DOI: 10.1007/s11136-020-02601-7
Abstract: To determine the usefulness of cumulative and additive risk models in predicting the healthy-related quality of life (HRQOL) of caregivers of youth with chronic gastrointestinal conditions. 203 caregivers (82.8% mothers; 77.3% white) of youth (M = 11.27… read more here.
Keywords: risk; cumulative risk; quality life; risk models ... See more keywords
Multivariate models for predicting glacier termini
Sign Up to like & getrecommendations! Published in 2017 at "Environmental Earth Sciences"
DOI: 10.1007/s12665-017-7135-2
Abstract: Concerns over the rapid retreat rates of mountain glaciers have been rising as global temperatures have continued to increase. The extent of variation in the retreat of mountain glaciers can provide information about changes to… read more here.
Keywords: mountain glaciers; glacier termini; multivariate models; retreat ... See more keywords
Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies
Sign Up to like & getrecommendations! Published in 2019 at "Animal Feed Science and Technology"
DOI: 10.1016/j.anifeedsci.2019.114207
Abstract: The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle,… read more here.
Keywords: performance existing; performance; rsr; smallest rsr ... See more keywords
Adaptive learning based data-driven models for predicting hourly building energy use
Sign Up to like & getrecommendations! Published in 2018 at "Energy and Buildings"
DOI: 10.1016/j.enbuild.2017.10.054
Abstract: Abstract Accurately predicting energy usage in buildings is of great importance in various efforts on improving building energy efficiencies such as fault detection and diagnostics, building-grid interactions, and building commissioning. Data-driven approach and first-principle approach… read more here.
Keywords: hourly building; energy; data driven; building energy ... See more keywords
Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.
Sign Up to like & getrecommendations! Published in 2022 at "Chemical research in toxicology"
DOI: 10.1021/acs.chemrestox.1c00310
Abstract: The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed.… read more here.
Keywords: cytotoxicity; machine learning; learning models; cytotoxicity nanomaterials ... See more keywords
Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties
Sign Up to like & getrecommendations! Published in 2023 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c01662
Abstract: Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides… read more here.
Keywords: models predicting; learning models; vis spectra; machine learning ... See more keywords
Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase
Sign Up to like & getrecommendations! Published in 2024 at "ACS Omega"
DOI: 10.1021/acsomega.4c03834
Abstract: Continuous use of antibiotics leads to the ability of bacteria to adapt by developing complex antibiotic resistance (AR) mechanisms. The synthesis of β-lactamases is a widely observed AR mechanism. The class C β-lactamase (AmpC) causes… read more here.
Keywords: inhibitors ampc; models predicting; machine; noncovalent inhibitors ... See more keywords
Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-65367-9
Abstract: Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and… read more here.
Keywords: epidemiology; deep learning; learning models; predicting survival ... See more keywords