Machine Learning (Article)
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy
This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.
Fast-Track Program for COVID-19 Test Development and Distribution
Rapid Acceleration of #Diagnostics (RADx), is a fast-track technology development program that leverages the National Institutes of Health (NIH) Point-of-Care Technology Research Network #POCTRNN).
The need for a system view to regulate artificial intelligence/machine learning-based software as medical device
FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition
Made With ML
Main website: madewithml.com Summary Made With ML is a platform for the ML community to discover, build and share projects. Our goal is to create a learning space where all of the best ML content is tagged, organized and curated. And as you use the platform and learn, we guide you through building projects and …
Deep Learning or Machine Learning? (MathWorks)
“In this ebook, we discuss some of the key differences between deep learning and traditional machine learning approaches. We look at three factors that might influence your decision and then step through an example that combines the two approaches”. (MathWorks).
TPOT is a Python Automated Machine Learning tool
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning (AutoML) tool that optimizes machine learning pipelines using genetic programming.
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too.
Free and Open Machine Learning Documentation Release 1.0.1
This book is all about applying machine learning solutions for real practical use cases. This means the core focus is on outlining how to use machine learning in a simple way so you can benefit of this powerful technology.
Machine learning is an exciting and powerful technology. The continuous use and growth of machine learning technology opens new opportunities. This great technology should available to use for everyone. This means that everyone should be able to learn, play and create great applications using machine learning technology.