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

TL;DR

The paper highlights risks of false conclusions in personalized health tracking due to evaluation pitfalls and offers guidelines to ensure reliability in medical data analysis.

Contribution

It identifies specific pitfalls in evaluating machine learning for personalized medicine and proposes three strategies to prevent misleading results.

Findings

Traditional evaluation methods can lead to false conclusions in medical tracking.

Three suggestions are proposed to improve reliability in personalized health data analysis.

Awareness of evaluation pitfalls is crucial for trustworthy personalized medicine.

Abstract

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.