Open Access Open Badges Research

Live sequence charts to model medical information

Eric Aslakson1, Smadar Szekely2, Suzanne D Vernon4, Lucinda Bateman3, Jan Baumbach46 and Yaki Setty2456*

Author Affiliations

1 Poiema, LLC, 375 Chelsea Cir NE, Atlanta, GA, 30307, USA

2 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, 76100, Israel

3 Fatigue Consultation Clinic, 1002 E. South Temple, Suite 408, Salt Lake City, UT, 84102, USA

4 Max-Planck-Institut für Informatik, Computational Systems Biology, 66123, Saarbrücken, Germany

5 The CFIDS Association of America, PO Box 220398, Charlotte, NC, 28222-0398, USA

6 Saarland University, Campus E2.1, Saarbrücken, 66123, Germany

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Theoretical Biology and Medical Modelling 2012, 9:22  doi:10.1186/1742-4682-9-22

Published: 15 June 2012



Medical records accumulate data concerning patient health and the natural history of disease progression. However, methods to mine information systematically in a form other than an electronic health record are not yet available. The purpose of this study was to develop an object modeling technique as a first step towards a formal database of medical records.


Live Sequence Charts (LSC) were used to formalize the narrative text obtained during a patient interview. LSCs utilize a visual scenario-based programming language to build object models. LSC extends the classical language of UML message sequence charts (MSC), predominantly through addition of modalities and providing executable semantics. Inter-object scenarios were defined to specify natural history event interactions and different scenarios in the narrative text.


A simulated medical record was specified into LSC formalism by translating the text into an object model that comprised a set of entities and events. The entities described the participating components (i.e., doctor, patient and record) and the events described the interactions between elements. A conceptual model is presented to illustrate the approach. An object model was generated from data extracted from an actual new patient interview, where the individual was eventually diagnosed as suffering from Chronic Fatigue Syndrome (CFS). This yielded a preliminary formal designated vocabulary for CFS development that provided a basis for future formalism of these records.


Translation of medical records into object models created the basis for a formal database of the patient narrative that temporally depicts the events preceding disease, the diagnosis and treatment approach. The LSCs object model of the medical narrative provided an intuitive, visual representation of the natural history of the patient’s disease.

Medical modeling; Live sequence charts; Computational health informatics