AIMC Topic: Predictive Value of Tests

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Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing.

Digestive diseases and sciences Aug 31, 2017
INTRODUCTION: Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records.
Humans Algorithms Databases, Factual Prognosis Tomography, X-Ray Computed Electronic Health Records Natural Language Processing Data Mining United States Department of Veterans Affairs Veterans Health Ultrasonography Fatty Liver United States Magnetic Resonance Imaging Predictive Value of Tests
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Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project).

The American journal of cardiology Aug 30, 2017
Previous studies have demonstrated that cardiorespiratory fitness is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined ca...
Exercise Test Exercise Tolerance Cardiorespiratory Fitness Michigan Cardiovascular Diseases Middle Aged Risk Assessment Predictive Value of Tests Cause of Death Forecasting Retrospective Studies Humans Female Machine Learning Male Algorithms
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Predicting symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network in a pediatric population.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery Aug 29, 2017
PURPOSE: Artificial neural networks (ANN) are increasingly applied to complex medical problem solving algorithms because their outcome prediction performance is superior to existing multiple regression models. ANN can successfully identify symptomati...
Neural Networks, Computer Retrospective Studies Vasospasm, Intracranial Female Male Aneurysm, Ruptured Humans Adolescent Child, Preschool Child Population Surveillance Predictive Value of Tests Subarachnoid Hemorrhage
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Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.

Digestive diseases and sciences Aug 23, 2017
BACKGROUND: Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral.
Referral and Consultation Blood Cell Count Odds Ratio Predictive Value of Tests Area Under Curve Registries Data Mining Sex Factors Aged, 80 and over Adult Colorectal Neoplasms Male Middle Aged Colonoscopy Diagnosis, Computer-Assisted ROC Curve Age Factors Early Detection of Cancer Algorithms Female Reproducibility of Results Risk Factors Aged Humans Machine Learning
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An Ameliorated Prediction of Drug-Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features.

International journal of molecular sciences Aug 16, 2017
The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper,...
Humans Animals Support Vector Machine Predictive Value of Tests Models, Biological Drug Interactions Wavelet Analysis
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Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

BMC medical informatics and decision making Aug 14, 2017
BACKGROUND: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases.
Humans Hepatitis B Support Vector Machine Predictive Value of Tests Immunoassay Hepatitis C Data Mining
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Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.

Circulation research Aug 9, 2017
RATIONALE: Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies.
Middle Aged Cohort Studies Follow-Up Studies Aged Humans Ethnicity Predictive Value of Tests Aged, 80 and over Machine Learning Atherosclerosis Female Survival Rate Male Cardiovascular Diseases
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Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Aug 4, 2017
BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpe...
Case-Control Studies Time Factors Health Status Aged Predictive Value of Tests Video Recording Humans Adult Equipment Design Fitness Trackers Machine Learning Biomechanical Phenomena Signal Processing, Computer-Assisted Actigraphy Male Female Stroke Movement Middle Aged Reproducibility of Results Feasibility Studies Upper Extremity Activities of Daily Living Acceleration
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Gaussian process classification of superparamagnetic relaxometry data: Phantom study.

Artificial intelligence in medicine Jul 24, 2017
MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIO...
Early Detection of Cancer Predictive Value of Tests Magnetite Nanoparticles Signal-To-Noise Ratio Neoplasms, Experimental Machine Learning Animals Magnetics Phantoms, Imaging Algorithms Image Processing, Computer-Assisted Normal Distribution Computer Simulation Mice Numerical Analysis, Computer-Assisted Reproducibility of Results Contrast Media Magnetic Resonance Imaging
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Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

Journal of manipulative and physiological therapeutics Jul 21, 2017
OBJECTIVES: This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial ...
Middle Aged Severity of Illness Index Case-Control Studies Predictive Value of Tests Humans Disability Evaluation Life Style Iran Occupational Diseases Neural Networks, Computer Occupational Health Psychology Female Low Back Pain Risk Factors Industry Adult Male
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