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AMIA Annual Symposium

AMIA Annual Symposium. 2008-2010. The tag cloud is created from AMIA paper titles in the past 3 years using http ://www.wordle.net . Overview. Symposium introduction from it homepage

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AMIA Annual Symposium

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  1. AMIA Annual Symposium 2008-2010 The tag cloud is created from AMIA paper titles in the past 3 years using http://www.wordle.net.

  2. Overview • Symposium introduction from it homepage • The AMIA Annual Symposium is the world’s most comprehensiveannual meeting on biomedical and health informatics. … Demonstrations and partnershipsin Innovation allow for comprehensive presentation of advanced systems, including new developments and innovative uses of commercial systems. • The Annual Symposium is always held in Washington, DC around November. Participants are from research universities, industrial companies, hospitals, non-profit organizations, and government agencies. • Paper length is limited to 5 pages, and distinguished paper award is awarded to 5 papers each year.

  3. Themes • Clinical Decision Support, Outcomes, and Patient Safety • Clinical Research Informatics • Clinical Workflow and Human Factors • Consumer Informatics and Multimedia PHRs • Data Integration and Exchange • Data Mining, NLP, Information Extraction • EHRs and Achieving Meaningful Use • Global eHealth • Imaging Informatics • Informatics Education and Workforce Development • Informatics in Clinical Education • Policy and Ethical Issues • Public Health Informatics and Biosurveillance • Simulation and Modeling • Terminology and Standards • Translational Bioinformatics and Biomedicine

  4. Notable Award Winners * The authors are selected for being awarded twice for their papers in the three years.

  5. Selected Papers in this Review • Information needs • Physician Opinions of the Importance, Accessibility, and Quality of Health Information and Their Use of the Information (University of Alabama at Birmingham, Partners HealthCare System, and Brigham and Women’s Hospital) • NLP • Combining Structured and Free-text Data for Automatic Coding of Patient Outcomes (Stanford University) • Applications (outlier detection & clinical guideline development) • Conditional Outlier Detection for Clinical Alerting (University of Pittsburgh) • A Constraint Satisfaction Approach to Data-Driven Implementation of Clinical Practice Guidelines (University of Ottawa) • Temporal abstraction and visualization • Medical Temporal-Knowledge Discovery via Temporal Abstraction (Ben Gurion University, Israel) • Tools for Improving the Characterization and Visualization of Changes in Neuro-Oncology Patients (UCLA) • How the Social Web Supports Patient Experimentation with a New Therapy: The demand for patient-controlled and patient-centered informatics (Patientslikeme) • Visualizing Multivariate Time Series Data to Detect Specific Medical Conditions (University of Maryland at Baltimore County and Johns Hopkins University)

  6. Panjamapirom, A., Burkhardt, J. H., Volk, L. A., Rothschild, J. M., Bates, D. W., Glandon, G. L., and Berner, E. S. “Physician Opinions of the Importance, Accessibility, and Quality of Health Information and Their Use of the Information,” AMIA Annual Symposium Proceedings, 2010, pp. 46-50. • Rankings of information types by pediatricians (P) and physicians treating adults (A): * The ranking is summarized from a total of 62 primary care physicians.

  7. Saria, S., McElvain, G., Rajani, A. K., Penn, A. A., and Koller, D. L. “Combining Structured and Free-text Data for Automatic Coding of Patient Outcomes,” AMIA Annual Symposium Proceedings, 2010, pp. 712-716. • Integrating structured information such as medications, treatments and laboratory results into current NLP-based information extraction systems can significantly boost coding accuracy of patient outcomes Clinical features • Medications • Clinical events • Culture reports • Radiology reports Language features • Disease mentions • Negations • Uncertainty modifiers • Correlated words and phrases

  8. Hauskrecht, M., Valko, M., Batal, I., Clermont, G., Visweswaran, S., and Cooper, G. F. “Conditional Outlier Detection for Clinical Alerting,” AMIA Annual Symposium Proceedings, 2010, pp. 286-290. • Our conjecture is that the detection of anomalies corresponding to unusual patient management actions will help to identify medical errors. • Identifying conditional anomalies consists of three steps: • segmentation and transformation of temporal information in EHRs, a patient instancex • building of predictive models for different patient management actions, and action y • identify highly unusual actions in a given patient over the course of that patient’s clinical care Anom(x, y) = 1-P(y | x)

  9. Kuziemsky, C., O’Sullivan, D., Michalowski, W., Wilk, S., and Farion, K. “A Constraint Satisfaction Approach to Data-Driven Implementation of Clinical Practice Guidelines,” AMIA Annual Symposium Proceedings, 2008, pp. 540-544. • Clinical practice guidelines tend to emphasize population-oriented or general recommendations while implementing guidelines in practice requires more patient-centered or data-driven suggestions. • Follow-up questions: • How to develop clinical guidelines from data? • How to evaluate the data-driven clinical guidelines and comparing them with expert-developed clinical guidelines (especially in a patient-centered manner)? • How the data-driven clinical guidelines be packaged as knowledge services?

  10. Moskovitch, R., and Shahar, Y. “Medical Temporal-Knowledge Discovery via Temporal Abstraction,” AMIA Annual Symposium Proceedings, 2009, pp. 452-456. • An example of a temporal abstraction of glycosylated hemoglobin values into state-abstraction (Low, Medium, High) and trend-abstraction(Increase, Decrease, Stable) intervals.

  11. Hsu, W., and Taira, R. K. “Tools for Improving the Characterization and Visualization of Changes in Neuro-Oncology Patients,” AMIA Annual Symposium Proceedings, 2010, pp. 316-320. • Capturing how a patient’s medical problems change over time is important for understanding the progression of a disease, its effects, and response to treatment. • Attributes and possible values for existence and findings are identified by a semi-automated tool for each problem.

  12. Hsu, W., and Taira, R. K. “Tools for Improving the Characterization and Visualization of Changes in Neuro-Oncology Patients,” AMIA Annual Symposium Proceedings, 2010, pp. 316-320. the master problem list the querying panel the timeline the detail panel

  13. Frost, J. H., Massagli, M. P., Wicks, P., and Heywood, J. “How the Social Web Supports Patient Experimentation with a New Therapy: The demand for patient-controlled and patient-centered informatics,” AMIA Annual Symposium Proceedings (2008), 2008, pp. 217-221.

  14. Ordóñez, P., desJardins, M., Feltes, C., Lehmann, C. U., and Fackler, J. “Visualizing Multivariate Time Series Data to Detect Specific Medical Conditions,” AMIA Annual Symposium Proceedings (2008), 2008, pp. 530-534.

  15. Summary

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