Pilot trial of semiautomated medical note writing using lexeme hypotheses Abstract   Clinicians write a billion free text notes per year. These notes are typically replete with errors of all types. No established automated method can extract data from this treasure trove. The practice of medicine therefore remains haphazard and chaotic, resulting in vast economic waste.  The lexeme hypotheses are based on our analysis of how records are created. They enable a computer system to predict what issue a clinician will need to address next, based on the environment in which the clinician is working, and what responses the clinician has selected to date. The system uses a lexicon storing the issues (queries) and a range of responses to the issues. When the clinician selects a response, a text fragment is added to the output file.  In the first phase of this work, the notes of 69 returning hemophilia patients were scrutinized, and the lexicon was expanded to 847 lexeme queries and 7995 responses to enable the construction of completed notes.  The quality of lexemegenerated notes from 20 consecutive subjects was then compared to the cliniciansxe2x80x99 conventional clinic notes. The system generated grammatically correct notes. In comparison to the traditional clinic note, the lexemegenerated notes were more complete (88 % compared with 62 %), and had less typographical and grammatical errors (0.8 versus 3.5 errors per note). The system notes and traditional notes averaged about 800 words, but the traditional notes had a much wider distribution of lengths. The notecreation rate from marshalling the data to completion using the system averaged 80 wpm, twice as fast as the typical clinician can type.  The lexeme method generates more complete, grammatical and organized notes faster than traditional methods. The notes are completely computerized at inception, and they incorporate prompts for clinicians to address otherwise overlooked items. This pilot justifies further exploration of this methodology.

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.