AI Medical Compendium Topic

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Diabetes Mellitus

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Implementation of machine learning algorithms to create diabetic patient re-admission profiles.

BMC medical informatics and decision making
BACKGROUND: Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming...

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Journal of medical systems
Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney funct...

A New Era for Cyborg Science Is Emerging: The Promise of Cyborganic Beings.

Advanced healthcare materials
Living flesh, hacked beyond known biological borders, and sophisticated machineries, made by humans, are currently being united to address some of the impending challenges in medicine. Imagine biological systems made from smart biomaterials with the ...

Deep supervised learning with mixture of neural networks.

Artificial intelligence in medicine
Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single mo...

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

BMC medical informatics and decision making
BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of mach...

Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

PloS one
INTRODUCTION: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this pape...

Predictive models for diabetes mellitus using machine learning techniques.

BMC endocrine disorders
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...

Comparing information extraction techniques for low-prevalence concepts: The case of insulin rejection by patients.

Journal of biomedical informatics
OBJECTIVE: To comparatively evaluate a range of Natural Language Processing (NLP) approaches for Information Extraction (IE) of low-prevalence concepts in clinical notes on the example of decline of insulin therapy recommendation by patients.

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...