![]() Pickrell, W.O., Lacey, A.S., Thomas, R.H., Lyons, R.A., Smith, P.E., Rees, M.I.: Trends in the first antiepileptic drug prescribed for epilepsy between 20. Névéol, A., Zweigenbaum, P.: Clinical natural language processing in 2014: foundational methods supporting efficient healthcare. MIT Critical Data: Secondary Analysis of Electronic Health Records. Medicines and Healthcare products Regulatory Agency: New measures to avoid valproate exposure in pregnancy endorsed. McNabb, L., Laramee, R.S.: Survey of Surveys (SoS)-mapping the landscape of survey papers in information visualization. Liddy, E.: Natural Language Processing (2001) Lacey, A.S., Pickrell, W.O., Thomas, R.H., Kerr, M.P., White, C.P., Rees, M.I.: Educational attainment of children born to mothers with epilepsy. ![]() Koleck, T.A., Dreisbach, C., Bourne, P.E., Bakken, S.: Natural language processing of symptoms documented in free-text narratives of electronic health records: A systematic review. Iakovidis, I.: Towards personal health record: current situation, obstacles and trends in implementation of electronic healthcare record in Europe. Hogan, T., Hinrichs, U., Hornecker, E.: The elicitation interview technique: capturing people’s experiences of data representations. ![]() Gunter, T.D., Terry, N.P.: The emergence of national electronic health record architectures in the united states and Australia: models, costs, and questions. Gramazio, C.C., Laidlaw, D.H., Schloss, K.B.: Colorgorical: creating discriminable and preferable color palettes for information visualization. ![]() Glueck, M., Naeini, M.P., Doshi-Velez, F., Chevalier, F., Khan, A., Wigdor, D., Brudno, M.: PhenoLines: phenotype comparison visualizations for disease subtyping via topic models. Glueck, M., Hamilton, P., Chevalier, F., Breslav, S., Khan, A., Wigdor, D., Brudno, M.: PhenoBlocks: phenotype comparison visualizations. Glueck, M., Gvozdik, A., Chevalier, F., Khan, A., Brudno, M., Wigdor, D.: PhenoStacks: cross-sectional cohort phenotype comparison visualizations. Ĭunningham, H., Tablan, V., Roberts, A., Bontcheva, K.: Getting more out of biomedical documents with GATE’s full lifecycle open source text analytics. īrehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. ACM Press, New York, New York, USA (2015). In: Proceedings of the 2015 Workshop on Visual Analytics in Healthcare - VAHC ’15, vol. Domain expert partners from EHR analysis review the software and are involved in every phase from the initial design to evaluation.īernard, J., Sessler, D., Bannach, A., May, T., Kohlhammer, J.: A visual active learning system for the assessment of patient well-being in prostate cancer research. We demonstrate LetterVis with three case studies using anonymized clinic letters, revealing insight that is normally either time-consuming or impossible to observe. We provide a range of filtering and selection options to assist pattern finding and outlier detection. The tool includes customized visual designs and views for visualizing antiepileptic drugs (AEDs). Letters are processed using natural language processing techniques and explored in multiple linked interactive views providing different levels of abstraction. We describe a letter-space that facilities the visual exploration of content and patterns inside a letter. This paper presents a novel visualization tool, LetterVis, to support the analysis of clinic letters through advanced interactive visual designs and queries. This increases the workload of analyzing these letters, performing individual and collective analysis, and clinical decision making. Clinicians often compose detailed clinic letters to record as much essential information during consultations as they can. The number of electronic health records (EHRs) collected by healthcare providers is growing at an unprecedented pace.
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