AIMC Topic:
Databases, Factual
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Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.
Human brain mapping
May 4, 2020
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to ...
Knowledge Graph-Enabled Cancer Data Analytics.
IEEE journal of biomedical and health informatics
May 4, 2020
Cancer registries collect unstructured and structured cancer data for surveillance purposes which provide important insights regarding cancer characteristics, treatments, and outcomes. Cancer registry data typically (1) categorize each reportable can...
Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR.
Journal of vascular and interventional radiology : JVIR
May 4, 2020
PURPOSE: To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR.
Child, Preschool
Child
Hospital Mortality
United States
Infant
Length of Stay
Adolescent
Time Factors
Portasystemic Shunt, Transjugular Intrahepatic
Iatrogenic Disease
Pneumothorax
Uterine Artery Embolization
Inpatients
Data Mining
Databases, Factual
Radiography, Interventional
Risk Factors
Retrospective Studies
Image-Guided Biopsy
Aged
Female
Aged, 80 and over
Infant, Newborn
Young Adult
Humans
Adult
Machine Learning
Male
Middle Aged
Risk Assessment
The influence of preprocessing on text classification using a bag-of-words representation.
PloS one
May 1, 2020
Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text pre...
Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.
The American journal of pathology
Apr 29, 2020
The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that o...
Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Computers in biology and medicine
Apr 28, 2020
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global econom...
Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble.
BioMed research international
Apr 27, 2020
Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart ...
A convolutional neural network to detect scoliosis treatment in radiographs.
International journal of computer assisted radiology and surgery
Apr 26, 2020
PURPOSE: The aim of this work is to propose a classification algorithm to automatically detect treatment for scoliosis (brace, implant or no treatment) in postero-anterior radiographs. Such automatic labelling of radiographs could represent a step to...
A classification-based approach to semi-supervised clustering with pairwise constraints.
Neural networks : the official journal of the International Neural Network Society
Apr 25, 2020
In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification ta...
A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model.
Medical & biological engineering & computing
Apr 23, 2020
The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. The proposed predictive model was then validated with the two most commonly used risk of readmission m...
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