AIMC Topic: False Positive Reactions

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DeepCNV: a deep learning approach for authenticating copy number variations.

Briefings in bioinformatics Sep 2, 2021
Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably hig...
Humans Deep Learning Datasets as Topic Genome, Human False Positive Reactions Disease ROC Curve DNA Copy Number Variations Area Under Curve Benchmarking
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Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images.

Technology in cancer research & treatment Jan 1, 2021
Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by pathologists, mainly due to its cellular morphology and diffuse invasion manner, and it has poor prog...
False Negative Reactions False Positive Reactions Computational Biology Area Under Curve Carcinoma, Signet Ring Cell Supervised Machine Learning Humans Stomach Neoplasms Deep Learning ROC Curve
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Alexa, do I have COVID-19?

Nature Oct 1, 2020
Humans Autism Spectrum Disorder Pulmonary Disease, Chronic Obstructive Depression Dementia Machine Learning Pandemics COVID-19 Biomarkers False Positive Reactions Voice Speech Disorders Pneumonia, Viral Mobile Applications Privacy Mass Screening Coronavirus Infections Parkinson Disease Dyspnea Triage Speech Recognition Software
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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...
False Positive Reactions Hemolysis Hematologic Tests Machine Learning Point-of-Care Systems Point-of-Care Testing Potassium Humans
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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...
Humans Machine Learning Sensitivity and Specificity Neoplasms Polymorphism, Single Nucleotide High-Throughput Nucleotide Sequencing 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.
False Positive Reactions Diagnostic Techniques, Ophthalmological Photography Glaucoma, Open-Angle Predictive Value of Tests Area Under Curve Fundus Oculi Optic Nerve Diseases Humans Databases, Factual Female Sensitivity and Specificity Cross-Sectional Studies Male Deep Learning ROC Curve
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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 ...
Reproducibility of Results Sensitivity and Specificity Deep Learning Genome-Wide Association Study ROC Curve Lung Neoplasms Carcinoma, Non-Small-Cell Lung DNA Copy Number Variations Humans Algorithms Databases, Factual Genome, Human Area Under Curve False Positive Reactions High-Throughput Nucleotide Sequencing Exons In Situ Hybridization, Fluorescence Proto-Oncogene Proteins c-met Receptor, ErbB-2
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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...
False Positive Reactions Breast Neoplasms Mammography Radiographic Image Interpretation, Computer-Assisted Artificial Intelligence Breast Humans Retrospective Studies Female Sensitivity and Specificity
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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...
Sleep Apnea, Obstructive Diagnosis, Computer-Assisted False Positive Reactions Machine Learning Heart Rate Risk Electrocardiography Humans Nonlinear Dynamics Algorithms Ballistocardiography Decision Trees Sleep Reproducibility of Results Signal Processing, Computer-Assisted 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...
False Positive Reactions Support Vector Machine Machine Learning Computer Security Decision Making Algorithms Cloud Computing Area Under Curve Computers Bayes Theorem
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