CBN: Constructing a Clinical Bayesian Network based on Data from the Electronic Medical Record

Publication date: Available online 3 November 2018Source: Journal of Biomedical InformaticsAuthor(s): Ying Shen, Lizhu Zhang, Jin Zhang, Min Yang, Buzhou Tang, Yaliang Li, Kai LeiAbstractThe process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Specifically, we first extract medical entity relationships from over 10,000 deidentified patient records and adopt the odds ratio (OR value) calculation and the K2 greedy algorithm to automatically construct a Bayesian topology. Then, Bayesian estimation is used for the probability distribution. Finally, we employ a Bayesian network to complete the causal relationship and probability distribution of ontology to enhance the ontology inference capability. By evaluating the learned topology versus the expert opinions of physicians and entropy calculations and by calculating the ontology-based diagnosis classification, our study demonstrates that the direct and automated ...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research