There is extreme noise in the patient annotations: for instance, in their health rhythm labels there’s around 20% contradictions in the dataset. accurate diagnosis limiting “domain rule violations” approach take your dataset, and validate rules IF validation is successful, train model normally with that sample IF the validation is unsuccessful, then use the output samples as negative examples