Detecting patient mix-up on blood samples with machine learning

Abstract

Patient mix-up on blood samples is one of the common causes of blood test errors. It is also known as patient misidentification problem. Although the detection of mix-up is commonly performed by naive comparison with the last laboratory results of the same patients: delta checks, either the sensitivity or the specificity of delta checks is not satisfactory. To establish a new detection system, we made simulated mix-up data from actual data of blood cell counts (CBC) and serum chemistry in our hospital. Using differences from the previous laboratory results as features, a highly accurate detection system was built by machine learning technique. An XGBoost model recorded the best ROC AUC score of 0.9986.