AIMC Topic: Support Vector Machine

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Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Nov 1, 2018
AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from ...
Phenotype Retrospective Studies Reproducibility of Results Risk Factors Time Factors Humans Predictive Value of Tests Signal Processing, Computer-Assisted Long QT Syndrome Heart Conduction System Heart Rate Genetic Predisposition to Disease Electrocardiography Support Vector Machine Action Potentials
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A systematic exploration of [Formula: see text] cutoff ranges in machine learning models for protein mutation stability prediction.

Journal of bioinformatics and computational biology Oct 1, 2018
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies ar...
Support Vector Machine Proteins Mutation Protein Stability Random Allocation Amino Acid Substitution Neural Networks, Computer Machine Learning Algorithms Computational Biology
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Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related ...
Pharmacovigilance Social Media Natural Language Processing Drug-Related Side Effects and Adverse Reactions Data Mining Humans Neural Networks, Computer Support Vector Machine
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Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

Clinical orthopaedics and related research Oct 1, 2018
BACKGROUND: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artific...
Humans Neural Networks, Computer Decision Support Techniques Prognosis Aged, 80 and over Young Adult Adult Male Middle Aged Retrospective Studies Female Reproducibility of Results Aged Predictive Value of Tests Bayes Theorem SEER Program United States Chondrosarcoma Support Vector Machine Diagnosis, Computer-Assisted Bone Neoplasms Adolescent Decision Trees Time Factors Child
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Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Journal of digital imaging Oct 1, 2018
Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ ...
Image Processing, Computer-Assisted Support Vector Machine Liver Algorithms Humans Tomography, X-Ray Computed Neural Networks, Computer Lung
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Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach.

Medicine Sep 1, 2018
The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM.A total of 50 D...
Humans Support Vector Machine Image Interpretation, Computer-Assisted Machine Learning Pattern Recognition, Automated Retrospective Studies Adult Cardiomyopathy, Dilated Reproducibility of Results Female Male Middle Aged Aged
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Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy.

Schizophrenia bulletin Aug 20, 2018
Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed t...
Support Vector Machine Magnetic Resonance Imaging Schizophrenia Connectome Young Adult Adult Male Image Processing, Computer-Assisted Humans Brain Female Reproducibility of Results
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Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.

Schizophrenia bulletin Aug 20, 2018
BACKGROUND: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' r...
Prognosis Female Young Adult Humans Brain Support Vector Machine Adult Schizophrenia Magnetic Resonance Imaging Male Outcome Assessment, Health Care Middle Aged Transcranial Magnetic Stimulation Prefrontal Cortex
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Predictive markers of depression in hypertension.

Medicine Aug 1, 2018
Hypertension and depression, as 2 major public health issues, are closely related. For patients having hypertension, in particular, depression is a risk factor for mortality and jeopardizes their wellbeing. The aim of the study is to apply support ve...
Risk Factors Blood Urea Nitrogen Humans Lipids Aged Uric Acid Case-Control Studies Support Vector Machine Biomarkers Creatine Kinase Depression Male Age Factors Sex Factors Female Cholesterol Blood Glucose Middle Aged Body Temperature Reproducibility of Results Hypertension Serum Albumin
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CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.
Software Protein Binding Computational Biology Protein Conformation Sequence Analysis, Protein Support Vector Machine Proteins Models, Molecular Vibrio cholerae Bacterial Proteins Acetyltransferases Coenzyme A
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