Machine Learning and its Applications in Medicine and Healthcare – February Energized Labs
Ed Wilkes provides both a great introduction to Machine Learning, and more technical information on its applications in medicine and healthcare in this video from Energized Labs.
To begin, Ed equips us with essential background knowledge on what Machine Learning is, the tools that can be used, the algorithms available and the vast range of opportunities for Machine Learning in medicine and healthcare. He goes on to clarify the difference between the terms Machine Learning, Artificial Intelligence and Data Science before providing some fascinating cases showing what can be done with Machine Learning, including Google’s DeepMind AI making history by defeating the Go champion and the use of generative adversarial networks (GANs) in upscaling images.
Continuing on from this, Ed then gives Machine Learning examples. Keeping it simple by using linear regression, he demonstrates the real crux of Machine Learning (the ability to predict new values). He then talks about classification problems, which are especially pertinent in medicine and provides an insight into the very intuitive Machine Learning tool, the Decision Tree. Neural networks are considered next, highly capable models based on how the human brain works. Ed then demonstrates training and test models, discussing the factors that must be taken into consideration when looking at performance metrics.
Moving on, Ed goes into an in-depth presentation of how Machine Learning is used in healthcare, for the detection of strokes, pneumonia, in laboratory and clinical diagnostics and retinal imaging. He reminds us that a lot of data is required to make robust models and that algorithms are highly dependant on the quality of data provided. Ed concludes with a brief look at the wider implications of Machine Learning and his predictions for the future. Watch Ed’s video now for a fascinating insight in how machine learning is shaping the future in medicine and healthcare.