AI Medical Compendium Topic:
Data Mining

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RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

BMC bioinformatics Feb 28, 2017
BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recog...
Machine Learning Algorithms Protein Binding RNA-Binding Proteins Neural Networks, Computer Nucleotide Motifs Sequence Analysis, RNA Data Mining RNA Binding Sites
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A novel hierarchical selective ensemble classifier with bioinformatics application.

Artificial intelligence in medicine Feb 27, 2017
Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization a...
Protein Interaction Maps Computational Biology Databases, Genetic Proteins Machine Learning Reproducibility of Results ROC Curve Area Under Curve Data Mining RNA, Transfer
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Classifier transfer with data selection strategies for online support vector machine classification with class imbalance.

Journal of neural engineering Feb 13, 2017
OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the sup...
Brain-Computer Interfaces Online Systems Computer Simulation Models, Neurological Reproducibility of Results Humans Sensitivity and Specificity Algorithms Electroencephalography Support Vector Machine Brain Data Mining Pattern Recognition, Automated
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Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports.

Artificial intelligence in medicine Feb 10, 2017
OBJECTIVE: Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporti...
Drug-Related Side Effects and Adverse Reactions Data Mining Humans Adverse Drug Reaction Reporting Systems Machine Learning Bayes Theorem Drug Interactions
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Controlling testing volume for respiratory viruses using machine learning and text mining.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children wi...
Respiratory Tract Infections Virus Diseases Viruses Female Infant ROC Curve Humans Hospitalization Adolescent Models, Theoretical Machine Learning Sensitivity and Specificity Child, Preschool Decision Trees Retrospective Studies Male Child Data Mining Electronic Health Records
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Bayesian Machine Learning Techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
The objective of the study is to assess the predictive performance of three different techniques as classifiers for extra-intestinal manifestations in 152 patients with Crohn's disease. Naïve Bayes, Bayesian Additive Regression Trees and Bayesian Net...
Machine Learning Algorithms Crohn Disease Female Male Risk Factors Humans Data Mining Genetic Predisposition to Disease Polymorphism, Genetic Models, Statistical Bayes Theorem
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Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.

Computer methods and programs in biomedicine Feb 9, 2017
BACKGROUND AND OBJECTIVES: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can d...
Safety-Based Drug Withdrawals Linear Models Pattern Recognition, Automated Humans False Positive Reactions Algorithms Drug Approval Patient Safety Computer Simulation Risk Benzene Sensitivity and Specificity Drug Monitoring Product Surveillance, Postmarketing Support Vector Machine Pharmaceutical Preparations Nervous System Carbon Data Mining Ethylamines Area Under Curve Toluene
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Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

Journal of vascular surgery Feb 8, 2017
OBJECTIVE: Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative n...
Databases, Factual Retrospective Studies Electronic Health Records Humans Peripheral Arterial Disease Lower Extremity Ankle Brachial Index Algorithms Models, Statistical International Classification of Diseases Natural Language Processing Administrative Claims, Healthcare Data Mining Minnesota
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Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

AJR. American journal of roentgenology Jan 31, 2017
OBJECTIVE: The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices.
Magnetic Resonance Imaging Natural Language Processing Workload Data Mining Academic Medical Centers Pattern Recognition, Automated Image Enhancement California Machine Learning Reproducibility of Results Sensitivity and Specificity Support Vector Machine Humans Radiology Information Systems North Carolina Radiology Department, Hospital Knee
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Disorder recognition in clinical texts using multi-label structured SVM.

BMC bioinformatics Jan 31, 2017
BACKGROUND: Information extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in cli...
Humans Support Vector Machine Data Mining Disease
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