AIMC Topic: Aged, 80 and over
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A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.
Computational and mathematical methods in medicine
Oct 20, 2019
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...
Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning.
European journal of radiology
Oct 17, 2019
PURPOSE: This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques.
Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.
JMIR mHealth and uHealth
Oct 16, 2019
BACKGROUND: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devic...
An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.
British journal of anaesthesia
Oct 15, 2019
BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores ...
Predictive models for diabetes mellitus using machine learning techniques.
BMC endocrine disorders
Oct 15, 2019
BACKGROUND: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to be...
Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.
Circulation. Cardiovascular quality and outcomes
Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Humans
Neural Networks, Computer
Machine Learning
Aged
Aged, 80 and over
Male
Middle Aged
Risk Assessment
Prognosis
Retrospective Studies
Electronic Health Records
Female
Risk Factors
Telemetry
Signal Processing, Computer-Assisted
Veterans Health Services
Stroke
Diagnosis, Computer-Assisted
Atrial Fibrillation
Time Factors
Proof of Concept Study
Logistic Models
Administrative Claims, Healthcare
United States
Predictive Value of Tests
A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.
Ultrasound in medicine & biology
Oct 11, 2019
The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different...
The Use of Transdermal Estrogen in Castrate-resistant, Steroid-refractory Prostate Cancer.
Clinical genitourinary cancer
Oct 10, 2019
BACKGROUND: Androgen-deprivation therapy is the mainstay of treatment for metastatic prostate cancer. Corticosteroids and estrogens are also useful agents in castration-resistant prostate cancer (CRPC). However, oral estrogens are associated with thr...
Promoting head CT exams in the emergency department triage using a machine learning model.
Neuroradiology
Oct 10, 2019
PURPOSE: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage.
Estimating treatment effects with machine learning.
Health services research
Oct 10, 2019
OBJECTIVE: To demonstrate the performance of methodologies that include machine learning (ML) algorithms to estimate average treatment effects under the assumption of exogeneity (selection on observables).
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