Did you know that when your doctor orders you to have a full blood count, most of the measurements made from your blood go to waste without every being recorded? This data represents an untapped goldmine of information that could be used to spot outbreaks of infectious diseases and patients at risk from particular diseases.
The BloodCounts! project is working to make best use of this information, using a form of artificial intelligence called machine learning. When it's operational it will be of the largest-scale applications yet of machine learning in medicine and healthcare.
Find out more about the BloodCounts! project, as well as the maths behind it, with the following articles.
Revolutionising the power of blood tests using AI — What is BloodCounts!, what will it do, and what are the challenges?
Mathematical snapshots: Daniel Kreuter — We talk to PhD student Daniel Kreuter about what it's like working with BloodCounts! as an early career researcher.
Maths in a minute: Machine learning and neural networks — A quick introduction to the type of algorithm that lies behind BloodCounts!
Maths in a minute: Semi-supervised machine learning — Machine learning started with supervised learning and us providing all the training materials, but we are finding ways for algorithms to learn with far fewer resources.