Improving Consumer Smartphone Based Disease Prevention with Machine Learning
Alistair Wickens Quealth
Alistair is convinced machine learning could offer significant advantages for disease prevention, particularly in eliciting new knowledge about the relationship between risk factors and disease.
Although his smartphone app, Quealth, is already referenced as one of the world’s most effective algorithms at predicting your risk of diabetes, Chronic Obstructive Pulmonary Disease (COPD) and Cardiovascular Disease (CVD), he believes this can be improved upon using machine learning techniques.
His observation is that many of the health apps on the market today use algorithms driven from large medical databases which hold patient information on risk factors and health outcomes. Researchers apply statistical techniques to determine the relationship between risk factors and disease. However this often oversimplifies what we all know — that health is a complex and multifactorial issue. This is where he believes machine learning can make a difference.
Based upon his experience Alistair will explore with the audience how he envisages the near-term future of machine learning for driving improved smartphone based disease prevention. He will particularly focus on improving the predictive capability of algorithm-driven models as well as lowering risk factors by more effective behaviour change.
His talk will be supported by scientific research conducted as well as outcome data from the smartphone app itself.