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Models, Statistical

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Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

Trends in molecular medicine Feb 4, 2018
There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' ...
Models, Statistical Substance-Related Disorders Machine Learning Biomarkers Biomedical Research Humans
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Statistical moments in modelling of swelling, erosion and drug release of hydrophilic matrix-tablets.

International journal of pharmaceutics Feb 1, 2018
Statistical moments were evaluated as suitable parameters for describing swelling and erosion processes (along with drug release) in hydrophilic controlled release matrix tablets. The effect of four independent formulation variables, corresponding to...
Models, Statistical Drug Carriers Polymers Kinetics Neural Networks, Computer Water Drug Compounding Linear Models Tablets Technology, Pharmaceutical Solubility Delayed-Action Preparations Drug Liberation Nimodipine Hydrophobic and Hydrophilic Interactions
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Max-margin weight learning for medical knowledge network.

Computer methods and programs in biomedicine Jan 11, 2018
BACKGROUND AND OBJECTIVE: The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The p...
Markov Chains Likelihood Functions Computer Graphics China Models, Theoretical Models, Statistical Signal Processing, Computer-Assisted Algorithms Electronic Health Records Reproducibility of Results Diagnosis, Computer-Assisted Humans Neural Networks, Computer Machine Learning
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An ensemble boosting model for predicting transfer to the pediatric intensive care unit.

International journal of medical informatics Jan 9, 2018
BACKGROUND: Early deterioration indicators have the potential to alert hospital care staff in advance of adverse events, such as patients requiring an increased level of care, or the need for rapid response teams to be called. Our work focuses on the...
Humans Intensive Care Units, Pediatric ROC Curve Models, Statistical Female Artificial Intelligence Health Services Needs and Demand Male Patient Transfer Severity of Illness Index Machine Learning Child, Hospitalized Child Algorithms Retrospective Studies
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Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Physics in medicine and biology Jan 9, 2018
Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). ...
Humans Models, Statistical Mammography Radiographic Image Interpretation, Computer-Assisted Adult Retrospective Studies Breast Breast Neoplasms Female Middle Aged Aged Breast Density Deep Learning Aged, 80 and over
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Assessing the impact of PM on respiratory disease using artificial neural networks.

Environmental pollution (Barking, Essex : 1987) Jan 5, 2018
Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression mo...
Particulate Matter Environmental Exposure Linear Models Humidity Models, Statistical Regression Analysis Air Pollutants Air Pollution Respiratory Tract Diseases Respiration Disorders Humans Neural Networks, Computer Temperature Hospitalization Brazil
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Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

PloS one Jan 5, 2018
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a...
Fuzzy Logic Models, Statistical Remote Sensing Technology Image Enhancement Machine Learning Support Vector Machine
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Quantitative Assessment of the Physiological Parameters Influencing QT Interval Response to Medication: Application of Computational Intelligence Tools.

Computational and mathematical methods in medicine Jan 4, 2018
Human heart electrophysiology is complex biological phenomenon, which is indirectly assessed by the measured ECG signal. ECG trace is further analyzed to derive interpretable surrogates including QT interval, QRS complex, PR interval, and T wave morp...
Myocytes, Cardiac Observer Variation Clinical Trials as Topic Cell Membrane Risk Electrophysiology Terfenadine Arrhythmias, Cardiac Ions Sodium Programming Languages Heart Potassium Anti-Arrhythmia Agents Calcium Reproducibility of Results Electrocardiography Humans Action Potentials Artificial Intelligence Models, Statistical Software Regression Analysis Algorithms
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Multiscale High-Level Feature Fusion for Histopathological Image Classification.

Computational and mathematical methods in medicine Dec 31, 2017
Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It c...
Principal Component Analysis Neural Networks, Computer Diagnosis, Computer-Assisted Area Under Curve Machine Learning ROC Curve Diagnostic Imaging Software Image Processing, Computer-Assisted False Positive Reactions Algorithms Models, Statistical Reproducibility of Results
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Transparent predictive modelling of the twin screw granulation process using a compensated interval type-2 fuzzy system.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V Dec 27, 2017
In this research, a new systematic modelling framework which uses machine learning for describing the granulation process is presented. First, an interval type-2 fuzzy model is elicited in order to predict the properties of the granules produced by t...
Technology, Pharmaceutical Models, Statistical Machine Learning Dosage Forms Particle Size Algorithms Stochastic Processes Models, Chemical Cellulose Drug Compounding Fuzzy Logic
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