  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.
