Knowledge Discovery with Bayesian Rule Learning for Actionable Biomedicine

Biomarker discovery is critical for both biomedical research and for clinical diagnostic, prognostic, and therapeutic decision-making. They help improve our understanding of the underlying physiological processes within an individual. Discovery of biomarkers from complex biomedical datasets is done using data mining algorithms. Hundreds of thousands of biomarkers have been discovered and reported in literature but only a few dozen have been found to be clinically useful. This discrepancy is because statistical significance is not clinical relevance. Statistical significance only accounts for the correctness of the learned associations. Clinical relevance, in addition to statistical significance, also accounts for clinical utility such as cost-effectiveness, non-invasiveness, efficacy, and safety of the proposed biomarkers. We need models that are statistically significant and clinically relevant, all the while keeping it interpretable. Interpretable classifiers are more actionable in medicine because they offer human-readable explanations for their predictions. Traditional data mining methods cannot account for clinical relevance. We formulate this as a knowledge discovery problem. In computer science, knowledge discovery in databases is “a non-trivial process of the extraction of valid, novel, potentially useful, and ultimately understandable patterns in data”. Bayesian Rule Learning (BRL) finds an optimal Bayesian network to explain the training data and translates that into an interpretable rule model. In this paper, we extend BRL for knowledge discovery (BRL-KD) to enable BRL to incorporate a clinical utility function to learn models that are clinically more relevant. We demonstrate this using a real-world dataset to predict cardiovascular disease outcome. We evaluate predictive performance with the area under the receiver operating characteristic curve (AUROC) and clinical utility with the cost of the model. We show that BRL-KD successfully generates a set of models offering different trade-offs between AUROC and cost. Based on the clinical standard, a model with an acceptable trade-off can then be chosen.