A Bayesian method for the automatic extraction of meaningful clinical sequences from large clinical databases

Clinical event recognition can have several applications, such as the examination of clinical stories that can be associated with negative hospital outcomes, or its use in clinical education to assist medical students recognize frequent clinical events.

The purpose of this study is to develop a non-annotated Bayes-based algorithm to extract useful clinical events from medical data.

We used subsets of MIMIC and CMS LDS datasets that include respiratory diagnoses to calculate two-itemset rules(one item in antecedent and one in consequent) which were used as building blocks for the construction of clinical event sequence order. The main condition for the event sequence is a sequential increase in the conditional probability of two-itemset rules having positive certainty factor, when they are studied together.A clinical event in our framework is defined to be a collection of several blocks of events that meet the aforementioned condition, when considered together. The correctness of our clinical sequences has been validated by two physicians.

Our results showed that medical experts scored the rules of this algorithm better than random Apriori rules. A GUI was designed that can be used to examine the association of each clinical event with the clinical outcomes of the length of stay, inpatient mortality, and hospital charges.

The present work provides a new approach on how we can improve extraction of clinical event sequences automatically, without user annotation. Our algorithm can successfully find, in several cases, blocks of rules which can tell correct clinical event stories.