Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Artificial Intelligence in Medicine
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Objective: In an effort to address the problem of laboratory errors, we develop and evaluate a method to detect mismatched specimens from nationally collected blood laboratory data in two experiments. Methods: In Experiments 1 and 2 using blood labs from National Health and Nutrition Examination Survey (NHANES) and values derived from the Diabetes Prevention Program (DPP) respectively, a proportion of glucose and HbA1c specimens were randomly mismatched. A Bayesian network that encoded probabilistic relationships among analytes was used to predict mismatches. In Experiment 1 the performance of the network was compared against existing error detection software. In Experiment 2 the network was compared against 11 human experts recruited from the American Academy of Clinical Chemists. Results were compared via area under the receiver-operator characteristic curves (AUCs) and with agreement statistics. Results: In Experiment 1 the network was most predictive of mismatches that produced clinically significant discrepancies between true and mismatched scores ((AUC of 0.87 (+/-0.04) for HbA1c and 0.83 (+/-0.02) for glucose), performed well in identifying errors among those self-reporting diabetes (N=329) (AUC=0.79 (+/-0.02)) and performed significantly better than the established approach it was tested against (in all cases p