AIMC Topic: Likelihood Functions

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Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach.

PLoS medicine Oct 12, 2021
BACKGROUND: Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of un...
Humans Machine Learning Delivery of Health Care Health Services Appointments and Schedules Outpatients Likelihood Functions England Cohort Studies Models, Theoretical
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Materials In Paintings (MIP): An interdisciplinary dataset for perception, art history, and computer vision.

PloS one Aug 26, 2021
In this paper, we capture and explore the painterly depictions of materials to enable the study of depiction and perception of materials through the artists' eye. We annotated a dataset of 19k paintings with 200k+ bounding boxes from which polygon se...
Humans Artificial Intelligence Likelihood Functions Time Factors Perception Paintings Databases as Topic Choice Behavior Statistics as Topic
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Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease.

Nature communications Jul 12, 2021
Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here w...
Humans Artificial Intelligence Machine Learning Organoids Likelihood Functions Mice, Inbred C57BL Dextran Sulfate Intestinal Mucosa Colitis Mice, Knockout Cohort Studies Gene Expression Regulation Inflammatory Bowel Diseases Mice Molecular Targeted Therapy Reproducibility of Results AMP-Activated Protein Kinases Animals Treatment Outcome Multigene Family Disease Models, Animal
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Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study.

Clinical journal of the American Society of Nephrology : CJASN Jul 12, 2021
BACKGROUND AND OBJECTIVES: Atrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident ...
Follow-Up Studies Age Factors Peptide Fragments Troponin T Risk Assessment Renal Insufficiency, Chronic Glomerular Filtration Rate Race Factors Atrial Fibrillation Natriuretic Peptide, Brain Likelihood Functions Machine Learning Biomarkers Female Male Middle Aged Humans Risk Factors Aged
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Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences May 10, 2021
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. -space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of -space...
Data Compression Knee Image Interpretation, Computer-Assisted Algorithms Linear Models Likelihood Functions Databases, Factual Brain Liver Deep Learning Benchmarking Magnetic Resonance Imaging Humans Neural Networks, Computer
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Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.

eLife Apr 6, 2021
In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by ...
Humans Computer Simulation Cognition Neural Networks, Computer Brain Neural Pathways Models, Neurological Likelihood Functions Bayes Theorem Cognitive Neuroscience
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A statistical framework for non-negative matrix factorization based on generalized dual divergence.

Neural networks : the official journal of the International Neural Network Society Mar 26, 2021
A statistical framework for non-negative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework, in...
Genomics Likelihood Functions Humans Machine Learning
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A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis.

Psychometrika Feb 2, 2021
Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-t...
Factor Analysis, Statistical Likelihood Functions Algorithms Deep Learning Psychometrics
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Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration.

Genetic epidemiology Aug 2, 2020
Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successfu...
Likelihood Functions Humans High-Temperature Requirement A Serine Peptidase 1 Machine Learning Algorithms Genome-Wide Association Study Models, Genetic Phenotype Diagnostic Errors Proteins Macular Degeneration United Kingdom
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Logistic regression with image covariates via the combination of L1 and Sobolev regularizations.

PloS one Jun 26, 2020
The use of image covariates to build a classification model has lots of impact in various fields, such as computer science, medicine, and so on. The aim of this paper is to develop an estimation method for logistic regression model with image covaria...
Logistic Models Likelihood Functions Machine Learning Phantoms, Imaging Computer Simulation Image Processing, Computer-Assisted
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