AIMC Topic: Cardiovascular Diseases

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Pulse-Wave-Pattern Classification with a Convolutional Neural Network.

Scientific reports
Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in patte...

Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improvi...

A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis.

Circulation. Cardiovascular imaging
BACKGROUND: Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent hum...

System-Pharmacology Dissection of Traditional Chinese herbs SINI Decoction for Treatment of Cardiovascular Diseases.

Anais da Academia Brasileira de Ciencias
Abstract: Cardiovascular diseases (CVDs) are leading causes of death in the world, owing to noticeable incidence and mortality. Traditional Chinese Medicine (TCM) SINI Decoction (SND) is used to prevent and treat CVDs, which has attracted extensive ...

Machine learning and blood pressure.

Journal of clinical hypertension (Greenwich, Conn.)
Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised learning algorithms that are being used to identify and predict blood pressure (BP) and other measu...

Development and verification of prediction models for preventing cardiovascular diseases.

PloS one
OBJECTIVES: Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to a...

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

PloS one
Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To impr...