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A new trend in medicine is the use of algorithms to analyze big datasets,e.g. using everything your phone measures about you for diagnostics ormonitoring. However, these algorithms are commonly compared against weakbaselines, which may contribute to excessive optimism. To assess how well analgorithm works, scientists typically ask how well its output correlates withmedically assigned scores. Here we perform a meta-analysis to quantify how theliterature evaluates their algorithms for monitoring mental wellbeing. We findthat the bulk of the literature ($\sim$77%) uses meaningless comparisons thatignore patient baseline state. For example, having an algorithm that uses phonedata to diagnose mood disorders would be useful. However, it is possible toover 80% of the variance of some mood measures in the population by simplyguessing that each patient has their own average mood - the patient-specificbaseline. Thus, an algorithm that just predicts that our mood is like itusually is can explain the majority of variance, but is, obviously, entirelyuseless. Comparing to the wrong (population) baseline has a massive effect onthe perceived quality of algorithms and produces baseless optimism in thefield. To solve this problem we propose "user lift" that reduces thesesystematic errors in the evaluation of personalized medical monitoring.

Meaningless comparisons lead to false optimism in medical machine learning