Linking Knowledge Discovery In Clinical Notes And Massive Biomedical Literature Repositories With the increase of adopting Electronic Health Records (EHR) in clinical practice, a promising venue where patient histories are analyzed to conduct clinical research has emerged. In the same time, the sheer number of biomedical literature introduced novel text mining methods that are able to analyze, structure, and summarize information and integrate it into various domainspecific databases and ontologies. However, novel approaches that complement and go beyond clinical and biomedical research are required in the domain of evidencebased medicine where the link between the two areas of research is established. In this research, we utilized NLP, text mining, and data mining methods to extract, analyze, and discover knowledge of clinical entitiesu0027 associations from clinical notes in EHR and link them with the biomedical literature to optimize the evidencebased medicine utilization.

Clinical Entities Association Rules (CLEAR): Untangling Clinical Notes in Electronic Health Records Clinical notes make a large part of Electronic Health Records (EHR). While applying large and automated analysis on unstructured clinical notes can be much more complex than on structured data, it can potentially provide better support and information for medical decisionmaking. In this paper, we propose a methodology to identify clinical entities and their associations that are widely exist across multiple types of clinical notes. The focus of this research is to discover patterns of significant relationships amongst clinical entities, such as treatments, tests, and diagnoses, particularly in clinical notes within EHR. Extracting clinical entities associations from clinical notes is a crucial task for many applications in clinical and public health informatics. We utilize populationlevel data to discover knowledge sets which can help health providers during their assessments for patients. We incorporate Natural Language Processing, text mining, and weighted association rule mining techniques in building a pipeline to transform clinical notes into knowledge set. Our preliminary results with proposed weighted clinical transactional itemset representation show promising results in identifying strongly related clinical entities. We evaluated our results with labeled data from a healthcare professional with an accuracy of 98.3% on the relevancy task and 54.4% of rules are labeled as interesting and worth further clinical investigation. We discovered patterns of patients developing secondary problems during their length of stay at a hospital.