Machine Learning Will Keep Us Healthy Longer

by Nayanah Siva, Wired.Co.UK

When assessing a patient, medics look at snapshots of physiological data that are manually taken by doctors or nurses, and make decisions against patient history, family background and test results, as well as their own knowledge and experience. But what if this data was constantly being taken, every second of every day? And what if a system was clever enough to compare these readings to thousands of patients worldwide with a similar history and disorder, as well as all the current clinical guidelines and studies, and make clinical suggestions to doctors?

In 2016, this kind of data-led decision-making will come ever closer. Sentrian, a California-based early-stage machine learning and biosensor analytics company for remote patient management, has created a system that does just that, and it’s currently being trialled on patients. “We actually don’t monitor people very frequently,” says Jack Kreindler, Sentrian’s founder and chief medical officer. “If I see a patient once a year, I may spend one hour listening to them, and the rest of the year’s 8,759 hours not listening to them. We are trying to build a system that will enable us to listen to the lives and bodies of patients all the time, so we can make better, earlier and more personalised decisions.”

Currently, wireless biosensors can collect simple data such as body temperature and heart rate as well as more complex information like oxygen saturation of the blood and potassium levels. Remote patient monitoring is typically done with one or two sensors at a time and the data is usually assessed by clinicians. But if a patient could constantly wear several sensors at a time, the amount of data produced would be enormous.

Sentrian’s approach collects data streams from biosensors and uses machine learning algorithms to detect subtle patterns based on general information within the system on chronic conditions. These can include heart disease, diabetes and chronic obstructive pulmonary disease (COPD). Data such as heart rate, blood pressure and oxygen saturation from wireless biosensors on the patient are pushed to a cloud-based engine that analyses this data and notifies doctors when needed. …. Read the article

DCL: There is a lot of CEP being used in the enabling technologies that allow this kind of real-time data collection. This is truly an Application Story.