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We demonstrate a validity problem of machine learning in the vitalapplication area of disease diagnosis in medicine. It arises when target labelsin training data are determined by an indirect measurement, and the fundamentalmeasurements needed to determine this indirect measurement are included in theinput data representation. Machine learning models trained on this data willlearn nothing else but to exactly reconstruct the known target definition. Suchmodels show perfect performance on similarly constructed test data but willfail catastrophically on real-world examples where the defining fundamentalmeasurements are not or only incompletely available. We present a generalprocedure allowing identification of problematic datasets and black-box machinelearning models trained on them, and exemplify our detection procedure on thetask of early prediction of sepsis.

Validity problems in clinical machine learning by indirect data labeling using consensus definitions