Clinical namedentity recognition: A short comparison The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMICIII database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with realclinical insights into patientu0027s health patterns and it may allow to create a first version of an annotated dataset.

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.