An example of how false conclusions could be made with personalized health tracking and suggestions for avoiding similar situations

Personalizing interventions and treatments is a necessity for optimal medical care. Recent advances in computing, such as personal electronic devices, have made it easier than ever to collect and utilize vast amounts of personal data on individuals. This data could support personalized medicine; however, there are pitfalls that must be avoided. We discuss an example, longitudinal medical tracking, in which traditional methods of evaluating machine learning algorithms fail and present the opportunity for false conclusions. We then pose three suggestions for avoiding such opportunities for misleading results in medical applications, where reliability is essential.Comment: Presented at the Data For Good Exchange 201