AIMC Topic: Predictive Value of Tests
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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...
Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.
Journal of medical toxicology : official journal of the American College of Medical Toxicology
Jun 1, 2018
Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of such vulnerable patients. Current practice, however, is limited by inaccurate reporting of medication...
Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features.
Journal of neuroscience methods
May 30, 2018
BACKGROUND: Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of ...
Heart disease diagnosis based on mediative fuzzy logic.
Artificial intelligence in medicine
May 30, 2018
Mediative fuzzy logic is an approach able to deal with inconsistent information providing a solution when contradiction exists. The aim of this paper is to design an expert system based on this type of fuzzy logic in order to diagnose a possible hear...
Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.
BMC cancer
May 30, 2018
BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. Howev...
Receptors, Estrogen
Models, Biological
Supervised Machine Learning
Cell Nucleus
Humans
ROC Curve
Young Adult
Principal Component Analysis
Adult
Neoplasm Staging
Image Processing, Computer-Assisted
Breast Neoplasms
Genetic Testing
Staining and Labeling
Middle Aged
Breast
Predictive Value of Tests
Prognosis
Female
Risk Factors
Aged
Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.
AJR. American journal of roentgenology
May 24, 2018
OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers.
Prediction of central neuropathic pain in spinal cord injury based on EEG classifier.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
May 23, 2018
OBJECTIVES: To create a classifier based on electroencephalography (EEG) to identify spinal cord injured (SCI) participants at risk of developing central neuropathic pain (CNP) by comparing them with patients who had already developed pain and with a...
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