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Personalizing interventions and treatments is a necessity for optimal medicalcare. Recent advances in computing, such as personal electronic devices, havemade it easier than ever to collect and utilize vast amounts of personal dataon individuals. This data could support personalized medicine; however, thereare pitfalls that must be avoided. We discuss an example, longitudinal medicaltracking, in which traditional methods of evaluating machine learningalgorithms fail and present the opportunity for false conclusions. We then posethree suggestions for avoiding such opportunities for misleading results inmedical applications, where reliability is essential.

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