The Modern Machine Age: What’s Next for Big Data and Health?

By Sarah Kunkle

Machine learning is no longer an abstract idea of the future. Google’s recent shareholder meeting and I/O conference both honed in on this concept as integral to their operations. While we await the release of self-driving cars, machine learning already touches many aspects of our everyday lives, including perpetuating Netflix binges and Amazon shopping sprees – yet most people are unsure, perhaps even a bit fearful, of exactly what this technology entails.

Machine learning refers to the science of getting computers to learn and act without explicitly being programmed. This field dates back to the 1950s, when Arthur Samuel taught a computer to beat a human opponent in a game of checkers. In addition to Netflix and Amazon, other well-known applications include Google News and Facebook’s friend suggestions.

Healthcare is now catching up to other industries like retail, finance, and social media in utilizing machine learning algorithms to address pressing issues such as medication adherence, mental health, cancer, and diabetes treatments. After winning Jeopardy in 2011, IBM’s Watson “moved on to medical school” and has partnered with notable institutions including Cleveland Clinic, Johnson & Johnson, Medtronic, and Memorial Sloan Kettering Cancer Center.

The explosion of mobile device use and personalized health technologies opened the door for machine learning to be used in health promotion rather than just disease treatment. A recent study of a health-tracking app using such algorithms provided preliminary evidence of efficacy in health promotion. The app automatically translates behavioral data into personalized suggestions that promote healthier lifestyle without any human involvement. Smartphones and wearables (e.g. FitBit, Apple Watch) are generating huge amounts of increasingly complex data – and we need the help of machines to make sense of it.

While many experts see machine learning as a huge opportunity to translate big data into new products and services to improve individual and population health, prominent figures like Steven Hawking, Bill Gates, and Elon Musk have voiced concerns about artificial intelligence . Using machine learning with health-related data is particularly tricky given legal and ethical challenges like privacy and equity. Recognizing these concerns, the Vitality Institute is organizing a workshop and consultation on these issues as pertaining to personalized health technology in partnership with the Institute of Medicine. If we want to harness the powers of big data and machine learning for health promotion and chronic disease prevention, continued dialogue on this issue is necessary.

Do you have any thoughts or concerns on the integration of machine learning, big data, and health? We would love to hear from you! Tweet at the Vitality Institute @VitalityInst or Sarah Kunkle @SarEve.


Image Credit: Leandro Castelao via The Economist 

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