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

• Published in: Computer methods and programs in biomedicine Vol. 233; p. 107392
• Main Authors: Shrestha, Aashara, Zikos, Dimitrios, Fegaras, Leonidas, Blebea, John, Sasso, Robert A.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2023
• Subjects: Algorithms; Association rule mining; Bayes Theorem; Clinical decision support; Data Mining - methods; Databases, Factual; Electronic Health Records; Humans; Probability; Sequential/ temporal event extraction; Sequential/ temporal event extraction; Association rule mining; Clinical decision support; Electronic health records
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107392

•A plug-in for standalone association mining algorithm to generate temporal clinical rules.•Manual annotation of clinical events not required.•A graphical user interface allows users to create and unfold clinical events.•Automatic clinical event sequence generated by using Bayesian method.•Clinical sequences produced by the plugin is verified by medical physicians.•Providing medical aid and medical education are some practical applications of this algorithm. Background: 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. Objective: The purpose of this study is to develop a non-annotated Bayes-based algorithm to extract useful clinical events from medical data. Materials and Methods: 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. Results: 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. Conclusion: 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.