Association Rule Mining from large datasets of clinical invoices document The concept of massive data generation nowadays affects several domains such as marketing including electronic invoices of large retailers, web access log files, healthcare, life sciences and so on. All these web activities introduced a new way to pay through the concept of electronic invoices (eInvoice), replacing the paper invoices. For these reasons, eInvoicing can be thought of as an innovative digital infrastructure for the issue, transmission, and storage of invoices. The availability of large volumes of eInvoices allows the discovery of new knowledge through data mining in these domains. Thus, users by using data mining can extract knowledge from large invoices documents. In this paper, we present a software tool for mining association rules from invoices produced in healthcare centers. In particular, the tool adopt a novel preprocessing methodology that provides merging, cleaning, formatting and summarization of eInvocies. The methodology can improve the quality of a huge amount of clinical invoices reducing the quantity of irrelevant data, making the remaining data suitable to mine information in form of association rules. The core of the tool allows to extract association rules from eInvoices; as a case study, we discuss the mined rules, highlighting the relationships among the purchased goods.

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.