AIMC Topic: Michigan

Clear Filters Showing 1 to 7 of 7 articles

The Promise of Remote Patient Monitoring.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association Nov 13, 2024
The promise of remote patient monitoring (RPM) lies in its ability to revolutionize health care delivery by enabling continuous, real-time tracking of patient health outside traditional clinical settings. The COVID-19 pandemic accelerated the adoptio...
Artificial Intelligence Pandemics COVID-19 Humans Monitoring, Physiologic Telemedicine Michigan SARS-CoV-2 Rural Population Health Services Accessibility
View on PubMed DOI

Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.

Accident; analysis and prevention Jul 16, 2020
Traditional methods for identifying crash-prone roadways are mainly based on historical crash data. It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment. However, the emerging connected vehicl...
Automobiles Accidents, Traffic Data Collection Reproducibility of Results Deep Learning Risk Michigan Humans Neural Networks, Computer Technology
View on PubMed DOI

Mechanism for temporal changes in exercise capacity after Fontan palliation: Role of Doppler echocardiography.

American heart journal Oct 22, 2017
BACKGROUND: The objective was to better understand Doppler hemodynamics and exercise capacity in patients with Fontan palliation by delineating the hemodynamic mechanism for temporal changes in their peak oxygen consumption (V̇o).
Academic Medical Centers Cohort Studies Double Outlet Right Ventricle Time Factors Young Adult Echocardiography, Doppler Humans Pulmonary Atresia Treatment Outcome Follow-Up Studies Exercise Test Adult Tricuspid Atresia Fontan Procedure Age Factors Retrospective Studies Male Oxygen Consumption Exercise Tolerance Heart Defects, Congenital Michigan
View on PubMed DOI

Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project).

The American journal of cardiology Aug 30, 2017
Previous studies have demonstrated that cardiorespiratory fitness is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined ca...
Humans Machine Learning Algorithms Forecasting Predictive Value of Tests Cause of Death Exercise Test Exercise Tolerance Cardiorespiratory Fitness Michigan Retrospective Studies Cardiovascular Diseases Female Middle Aged Male Risk Assessment
View on PubMed DOI

Supporting information retrieval from electronic health records: A report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE).

Journal of biomedical informatics May 13, 2015
OBJECTIVE: This paper describes the University of Michigan's nine-year experience in developing and using a full-text search engine designed to facilitate information retrieval (IR) from narrative documents stored in electronic health records (EHRs)....
Meaningful Use Software Validation Software Data Mining Electronic Health Records Michigan Natural Language Processing Search Engine
View on PubMed DOI

Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients With Liver Disease.

The American journal of gastroenterology Aug 1, 2020
INTRODUCTION: There is increasing recognition of the central role of muscle mass in predicting clinical outcomes in patients with liver disease. Muscle size can be extracted from computed tomography (CT) scans, but clinical implementation will requir...
Algorithms Prospective Studies Tomography, X-Ray Computed Humans Fibrosis Michigan Middle Aged Young Adult Deep Learning Cohort Studies Female Adolescent Adult Aged Muscle, Skeletal Male Predictive Value of Tests
View on PubMed DOI

Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.

IEEE transactions on medical imaging Mar 1, 2019
In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar auxiliary...
Area Under Curve Sample Size Michigan Machine Learning Breast Neoplasms Mammography Humans Neural Networks, Computer
View on PubMed DOI
Popular Topics
Recent Journals