Archived Talks

University of Pittsburgh Department of Biomedical Informatics Lecture Series

“Analyzing Biomedical Data Sets Using Executable Graphical Models”

Vojtech Huser, MD
Doctoral Candidate
University of Utah

Friday, December 7, 2007
11:00 AM to 12:00 NOON
Parkvale Building (200 Meyran Avenue), Classroom M-184 (on the mezzanine level)

Abstract: Clinical data warehouses accumulate large amounts of terminology-coded data. In addition to increased accumulation of data, higher data granularity, and longer time-spans, there is also an increasing demand for analysis of this data. For a nonexpert, the ability to analyze this data unaided is very limited. The seminar will present an analytical framework called RetroGuide (RG) that works with flowchart models. RetroGuide models can be extended with modular external applications, and executed on retrospective data. The RG framework was inspired by emerging workflow technology. Workflow technology offers several tools which support modeling, execution, and extensive analysis of IT or organizational processes.

The three specific aims covered by the presentation will be: (1) review of workflow technology and its current use, (2) development of the RG analytical framework which utilizes graphical, process-based modeling, and (3) evaluation of this framework using a series of case studies and a formal, comparison evaluation study.

RGs graphical representation format facilitates a stepwise, procedural approach to formulating analytical tasks. It uses a single patient execution model, and it resembles a manual chart review methodology. RG models can model complex temporal conditions and utilize external data manipulation, statistical, or reasoning technologies. The representation format is split into two layers, a flowchart and a code layer, which improves collaboration of analytical team members. Reports generated automatically by RG allow advanced drill-down capabilities, show in detail the models execution trail for each analyzed patient, and support iterative model improvements.

Three analytical domains of quality improvement, decision support development, and medical research were explored as part of the RG project (e.g., quality improvement problems in osteoporosis and cardiovascular patients, analysis of a computerized glucose management protocol, a problem in adverse drug event monitoring, or a research analysis of cancer patients). The case studies demonstrated RGs ability to support a wide range of complex analytical tasks, facilitate iterative exploration and review of electronic health record data, and provide a testing environment for retrospective simulation of analytical or decision support processes (using data from a real, large EDW).

http://en.wikipedia.org/wiki/RetroGuide

For more information: jxc3@pitt.edu or 412.647.7113