AIMC Topic: False Positive Reactions

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Detection of Falsely Elevated Point-of-Care Potassium Results Due to Hemolysis Using Predictive Analytics.

American journal of clinical pathology Jul 7, 2020
OBJECTIVES: Preanalytical factors, such as hemolysis, affect many components of a test panel. Machine learning can be used to recognize these patterns, alerting clinicians and laboratories to potentially erroneous results. In particular, machine lear...
Point-of-Care Testing Potassium False Positive Reactions Hemolysis Hematologic Tests Humans Machine Learning Point-of-Care Systems
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Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.

Clinical chemistry Jan 1, 2020
BACKGROUND: Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories r...
Machine Learning Sensitivity and Specificity Neoplasms Polymorphism, Single Nucleotide High-Throughput Nucleotide Sequencing Humans False Positive Reactions
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Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.

JAMA ophthalmology Dec 1, 2019
IMPORTANCE: A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON.
Diagnostic Techniques, Ophthalmological Photography Glaucoma, Open-Angle Cross-Sectional Studies Male Deep Learning Humans Predictive Value of Tests ROC Curve Databases, Factual Female Area Under Curve Sensitivity and Specificity Fundus Oculi Optic Nerve Diseases False Positive Reactions
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DL-CNV: A deep learning method for identifying copy number variations based on next generation target sequencing.

Mathematical biosciences and engineering : MBE Sep 30, 2019
Copy number variations (CNVs) play an important role in many types of cancer. With the rapid development of next generation sequencing (NGS) techniques, many methods for detecting CNVs of a single sample have emerged: (i) require genome-wide data of ...
Humans Algorithms Databases, Factual Reproducibility of Results Sensitivity and Specificity Carcinoma, Non-Small-Cell Lung DNA Copy Number Variations Area Under Curve High-Throughput Nucleotide Sequencing In Situ Hybridization, Fluorescence Deep Learning Genome-Wide Association Study ROC Curve Lung Neoplasms Proto-Oncogene Proteins c-met Receptor, ErbB-2 Genome, Human False Positive Reactions Exons
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Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD.

Journal of digital imaging Aug 1, 2019
The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study...
Mammography Radiographic Image Interpretation, Computer-Assisted Breast False Positive Reactions Humans Artificial Intelligence Retrospective Studies Female Sensitivity and Specificity Breast Neoplasms
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Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach.

Mathematical biosciences and engineering : MBE Jun 19, 2019
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disease that affects people's health, especially in the elderly. In the traditional PSG-based OSA detection, people's sleep may be disturbed, meanwhile the electrode slices are easil...
False Positive Reactions Risk Ballistocardiography Sleep Apnea, Obstructive Heart Rate Diagnosis, Computer-Assisted Nonlinear Dynamics Electrocardiography Sleep Decision Trees Signal Processing, Computer-Assisted Humans Machine Learning Algorithms Reproducibility of Results Sensitivity and Specificity
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TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis.

Mathematical biosciences and engineering : MBE Mar 26, 2019
The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way f...
Computer Security Cloud Computing Machine Learning Decision Making Algorithms Area Under Curve Computers Bayes Theorem Support Vector Machine False Positive Reactions
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Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

Investigative ophthalmology & visual science Mar 1, 2019
PURPOSE: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs).
Humans Macula Lutea Diabetic Retinopathy Ranibizumab Neural Networks, Computer Image Interpretation, Computer-Assisted Photography False Positive Reactions Middle Aged Intravitreal Injections Tomography, Optical Coherence Retrospective Studies Deep Learning Diagnostic Techniques, Ophthalmological Fundus Oculi Female Angiogenesis Inhibitors Macular Edema Sensitivity and Specificity Randomized Controlled Trials as Topic Vascular Endothelial Growth Factor A Predictive Value of Tests Male
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Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations.

Stroke Mar 1, 2019
Background and Purpose- Triaging of referrals to transient ischemic attack (TIA) clinics is aided by risk stratification. Deep learning-based natural language processing, a type of machine learning, may be able to assist with the prediction of cerebr...
Humans Neural Networks, Computer Machine Learning Female Aged Aged, 80 and over Area Under Curve Cerebrovascular Disorders Treatment Outcome False Positive Reactions Male Magnetic Resonance Imaging Ischemic Attack, Transient Middle Aged Predictive Value of Tests Risk Assessment Triage Deep Learning Natural Language Processing
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An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Investigative ophthalmology & visual science Jan 2, 2019
PURPOSE: To detect visual field (VF) progression by analyzing spatial pattern changes.
Predictive Value of Tests Visual Fields Visual Field Tests False Positive Reactions Spatial Processing Diagnosis, Computer-Assisted Cohort Studies Humans Follow-Up Studies Glaucoma Vision Disorders Artificial Intelligence Disease Progression
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