Machine learning: what's the diagnosis?

It also frees up healthcare resources and money if these people never even become patients. Heart attacks can occur with little to no warning and lead to 200 deaths a week in the UK; however, they are largely caused by patient behaviours, such as their diet. While doctors try to predict who is at risk based on health and behaviours, humans only have a 30% success rate. But now, a machine-learning algorithm created by Carnegie Mellon University looks at 72 parameters in patients' medical history including vital signs, age, blood glucose and platelet counts, and then assesses whether the patient is heading for a'Code Blue' attack. When tested on historic data the system was able to tell, sometimes 4 hours before the event, whether a patient would have gone into arrest at 80% accuracy.