AIMC Topic:
Predictive Value of Tests
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A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.
Gastrointestinal endoscopy
Jul 11, 2018
BACKGROUND AND AIMS: GI angiectasia (GIA) is the most common small-bowel (SB) vascular lesion, with an inherent risk of bleeding. SB capsule endoscopy (SB-CE) is the currently accepted diagnostic procedure. The aim of this study was to develop a comp...
Digital Image Analysis with Fully Connected Convolutional Neural Network to Facilitate Hysteroscopic Fibroid Resection.
Gynecologic and obstetric investigation
Jul 5, 2018
AIMS: The study aimed to determine the accuracy of deep neural network in identifying the plane between myoma and normal myometrium.
Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma.
Computers in biology and medicine
Jun 28, 2018
Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop ...
A meta-learning framework using representation learning to predict drug-drug interaction.
Journal of biomedical informatics
Jun 26, 2018
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...
An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.
Journal of the American Heart Association
Jun 26, 2018
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...
Decision Support Techniques
Prognosis
Retrospective Studies
Humans
Deep Learning
Adult
Time Factors
Heart Arrest
Early Diagnosis
Male
Female
Inpatients
Resuscitation
Vital Signs
Middle Aged
Reproducibility of Results
Predictive Value of Tests
Seoul
Risk Assessment
Risk Factors
Diagnosis, Computer-Assisted
Aged
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.
Gastroenterology
Jun 18, 2018
BACKGROUND & AIMS: The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate, which is estimated to be higher than 50% in the screening-age population. ...
Adenomatous Polyps
ROC Curve
Early Detection of Cancer
Predictive Value of Tests
Area Under Curve
Video Recording
Image Interpretation, Computer-Assisted
Observer Variation
Feasibility Studies
Colonic Polyps
Reproducibility of Results
Colorectal Neoplasms
Colonoscopy
Diagnosis, Computer-Assisted
Humans
Neural Networks, Computer
Machine Learning
Prognosis
Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.
JACC. Cardiovascular imaging
Jun 13, 2018
OBJECTIVES: The goal of this study was to use machine learning to more accurately predict survival after echocardiography.
A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.
Scientific reports
Jun 11, 2018
Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal sam...
Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.
PloS one
Jun 7, 2018
Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant...
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.
Medical image analysis
Jun 2, 2018
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patien...
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