AIMC Topic: Logistic Models

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Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] Sep 26, 2018
OBJECTIVE: The aim of this study is to predict the risk of severe acute pancreatitis (SAP) associated with acute lung injury (ALI) by artificial neural networks (ANNs) model.
Predictive Value of Tests Area Under Curve Acute Disease L-Lactate Dehydrogenase Oxyhemoglobins Pancreatitis Male Middle Aged Risk Assessment ROC Curve Logistic Models Prognosis Acute Kidney Injury Female Young Adult Adult Neural Networks, Computer Machine Learning Algorithms Humans
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Modeling growth limits of Bacillus spp. spores by using deep-learning algorithm.

Food microbiology Sep 20, 2018
Growth/no growth boundary models for Bacillus spores that accounted for the effects of environmental pH, water activity (a), acetic acid, lactic acid, bacterial strain, and storage period were developed using conventional logistic regression and mach...
Protein Hydrolysates Caseins Acetic Acid Water Logistic Models Lactic Acid Spores, Bacterial Bacillus Algorithms Deep Learning
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Experience With Hypothermic Machine Perfusion in Expanded Criteria Donors: Functional Outcomes.

Transplantation proceedings Sep 18, 2018
UNLABELLED: Hypothermic machine perfusion (HMP) decreases delayed graft function (DGF) and improves 1-year graft survival in expanded criteria donors (ECDs). Time of HMP could be associated with incidence of DGF.
Tissue Donors Graft Survival Logistic Models Male Perfusion Multivariate Analysis Humans Middle Aged Proportional Hazards Models Odds Ratio Kidney Transplantation Cohort Studies Cryopreservation Female Delayed Graft Function Aged Adult
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Predicting the risk of acute care readmissions among rehabilitation inpatients: A machine learning approach.

Journal of biomedical informatics Sep 18, 2018
INTRODUCTION: Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predictive model based on machine learning algorithms to identify patients at high risk of readmission.
Middle Aged ROC Curve Logistic Models Support Vector Machine Longitudinal Studies Female Hospitalization Aged Sensitivity and Specificity Male Outpatients Rehabilitation Preventive Medicine Regression Analysis Inpatients Patient Readmission Risk North Carolina Humans Machine Learning Algorithms Retrospective Studies
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Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology Sep 14, 2018
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).
Brain Retrospective Studies Female Aged Adult Brain Neoplasms Male Middle Aged ROC Curve Logistic Models Lymphoma Humans Magnetic Resonance Imaging Diagnosis, Differential Machine Learning Glioblastoma Image Enhancement
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Serum vitamin D deficiency and vitamin D receptor gene polymorphism are associated with increased risk of cardiovascular disease in a Chinese rural population.

Nutrition research (New York, N.Y.) Sep 13, 2018
Several studies have reported a conflicting association between vitamin D deficiency, vitamin D receptor (VDR) polymorphism, and the risk of cardiovascular disease (CVD). We hypothesized that serum 25(OH)D concentrations and single nucleotide polymor...
China Blood Glucose Polymorphism, Single Nucleotide Humans Genotype Asian People Biomarkers Logistic Models Rural Population Triglycerides Haplotypes Aged Female Vitamin D Gene Frequency Adult Receptors, Calcitriol Male Vitamin D Deficiency Cardiovascular Diseases Alleles Middle Aged
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Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.

Journal of neural engineering Sep 13, 2018
OBJECTIVE: We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep learning) for the problem of predicting successful episodic memory encoding using direct brain recordin...
Humans Algorithms Computer Simulation Stochastic Processes Deep Learning Memory, Episodic Logistic Models Mental Recall Support Vector Machine Electrodes, Implanted Predictive Value of Tests Theta Rhythm Electroencephalography
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CGPS: A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways.

Journal of genetics and genomics = Yi chuan xue bao Sep 13, 2018
Gene set enrichment (GSE) analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE to...
Databases, Genetic Multiple Myeloma Base Sequence Precursor Cell Lymphoblastic Leukemia-Lymphoma Biosynthetic Pathways Machine Learning Oligonucleotide Array Sequence Analysis Software Antineoplastic Agents Panobinostat Computational Biology Logistic Models
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A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients.

BMC medical informatics and decision making Sep 4, 2018
BACKGROUND: Treatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among HIV positive individuals. Effective highly active antiretroviral therapy (HAART) should lead to undetectable viral load within 6 months of initiati...
Predictive Value of Tests CD4 Lymphocyte Count Antiretroviral Therapy, Highly Active Humans Machine Learning Female Adult Male Cohort Studies Logistic Models Treatment Outcome HIV Infections Viral Load Anti-HIV Agents
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Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.

European radiology Sep 3, 2018
OBJECTIVE: To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.
Machine Learning Algorithms Decision Support Techniques Prospective Studies Female Humans Logistic Models Neural Networks, Computer Liver Cirrhosis Support Vector Machine Decision Trees Area Under Curve Hepatitis B, Chronic Adult Male Middle Aged Ultrasonography ROC Curve
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