Improving clinical record visualization recommendations with Bayesian stream learning Position Paper

Clinical record integration and visualization is one of the most important abilities of modern health information systems (HIS). Its use on clinical encounters plays a relevant role in the efficacy and efficiency of healthcare. However, integrated HIS of central hospitals may gather millions of clinical reports (e.g. radiology, lab results, etc.). Hence, the clinical record must manage a stream of reports being produced in the entire hospital. Moreover, not all documents from a patient are relevant for a given encounter, and therefore not visualized during that encounter. Thus, the HIS must also manage a stream of events of visualization of reports, which runs in parallel to the stream of documents production. The aim of our project is to provide the physician with a recommendation of clinical reports to consider when they log in the computer. Our approach is to model relevance as the probability that a given document will be accessed in the current time frame. For that, we design a data stream management system to process the two streams, and Bayesian networks to learn those probabilities based on document, patient, department and user information. One of the biggest challenges to the learning problem, so far, is that no negative examples are produced by the stream (i.e. there are no record of documents not being visualized) leading to a one-class classification problem. The aim of this paper is to clearly present the setting and rationale for the approach. Current work is focused on both the stream processing mechanism and the Bayesian probability estimation.