AIMC Topic: False Positive Reactions

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Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

Computational and mathematical methods in medicine Dec 14, 2017
High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional me...
Nerve Net Neurology Brain Mapping False Positive Reactions Neural Pathways Depressive Disorder, Major Adolescent Support Vector Machine Magnetic Resonance Imaging Adult Brain Humans Male Female Machine Learning Middle Aged Reproducibility of Results Algorithms Young Adult
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Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.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...
Pharmaceutical Preparations Data Mining Humans Toluene Product Surveillance, Postmarketing Algorithms Linear Models Nervous System Patient Safety Ethylamines Carbon Computer Simulation Safety-Based Drug Withdrawals Area Under Curve Sensitivity and Specificity Pattern Recognition, Automated Drug Monitoring Benzene False Positive Reactions Support Vector Machine Drug Approval Risk
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Reducing false arrhythmia alarm rates using robust heart rate estimation and cost-sensitive support vector machines.

Physiological measurement Jan 18, 2017
To lessen the rate of false critical arrhythmia alarms, we used robust heart rate estimation and cost-sensitive support vector machines. The PhysioNet MIMIC II database and the 2015 PhysioNet/CinC Challenge public database were used as the training d...
Humans Time Factors Clinical Alarms Arrhythmias, Cardiac Heart Rate Electrocardiography False Positive Reactions Support Vector Machine Costs and Cost Analysis Signal Processing, Computer-Assisted
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A ℓ norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

Computer methods and programs in biomedicine Dec 15, 2016
OBJECTIVE: The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD).
Solitary Pulmonary Nodule False Positive Reactions Humans Machine Learning Diagnosis, Computer-Assisted Support Vector Machine
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Lung nodule classification using deep feature fusion in chest radiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Nov 12, 2016
Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment of lung nodules can significantly improve the quality of patients' lives. Because of their small size and the interlaced nature of chest ...
Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted False Positive Reactions Radiography, Thoracic Machine Learning Sensitivity and Specificity Lung Humans
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Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection.

IEEE transactions on bio-medical engineering Sep 26, 2016
OBJECTIVE: False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively add...
Sensitivity and Specificity Diagnostic Errors Imaging, Three-Dimensional Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Pattern Recognition, Automated False Positive Reactions Machine Learning Algorithms Tomography, X-Ray Computed Reproducibility of Results Humans Neural Networks, Computer
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Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 1, 2016
Diabetic retinopathy (DR) is a progressive disease, and its detection at an early stage is crucial for saving a patient's vision. An automated screening system for DR can help in reduce the chances of complete blindness due to DR along with lowering ...
Microaneurysm Humans Diabetic Retinopathy Area Under Curve Machine Learning Image Interpretation, Computer-Assisted ROC Curve Pattern Recognition, Automated False Positive Reactions
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Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals.

Physiological measurement Jul 25, 2016
False arrhythmia alarms pose a major threat to the quality of care in today's ICU. Thus, the PhysioNet/Computing in Cardiology Challenge 2015 aimed at reducing false alarms by exploiting multimodal cardiac signals recorded by a patient monitor. False...
Humans Machine Learning Arrhythmias, Cardiac Intensive Care Units Heart Rate Electrocardiography False Positive Reactions Monitoring, Physiologic Clinical Alarms Signal Processing, Computer-Assisted
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Reduction of false arrhythmia alarms using signal selection and machine learning.

Physiological measurement Jul 25, 2016
In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy en...
Critical Care False Positive Reactions Signal-To-Noise Ratio Clinical Alarms Monitoring, Physiologic Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Humans Machine Learning Electrocardiography
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Suppression of false arrhythmia alarms in the ICU: a machine learning approach.

Physiological measurement Jul 25, 2016
This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of ...
Humans Machine Learning Intensive Care Units Electrocardiography Heart Rate Monitoring, Physiologic Pattern Recognition, Automated Signal Processing, Computer-Assisted False Positive Reactions Arrhythmias, Cardiac Clinical Alarms
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