ICD encoded diagnoses are a popular criterion for eligibility algorithms for study cohort recruitment. However, “official” ICD encoded diagnoses used for billing purposes are afflicted with a bias originating from legal issues. This work presents an approach to estimate the degree of the encoding bias for the complete ICD catalogue at a German university hospital. The free text diagnoses sections of discharge letters are automatically classified using a supervised machine learning algorithm. The automatic classifications are compared with the official, manually classified codes. For selected ICD codes the approach works sufficiently well.