Aims Automated checks for medication‐related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug–drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug–disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses. Methods We identified drugs that are associated with diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert‐based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real‐life sample of medication and diagnosis data excerpts from 3506 discharge records of geriatric patients. Results Depending on the algorithm, the desired focus (i.e., sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0–99.8% (95% CI 41.7–100.0%) and a sensitivity of 3.8–83.1% (95% CI 1.0–86.1%). Using only medication data, we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses. Conclusion This study provides a proof of principle that some key diagnosis‐related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug–disease contraindications in community pharmacy or clinical routine data.