Abstract
We present a practical framework for visual exploration of co-morbidities between diseases. By utilizing high-quality multilevel layout and clustering algorithms, we have implemented an innovative two-layer multiplex network of human diseases. Specifically, we extract the International Classification of Diseases, Ninth Revision (ICD9) codes from an Electronic Medical Records (EMRs) database to build our map of human diseases. In the lower layer, the abbreviated disease terms of ICD9 codes in the irregular regions look like cities in geographical maps. The connections represent the disease pairs co-morbidities, calculated by using co-occurrence. In the upper layer, we visualize multi-object profile of clinical information. For practical application, we propose an interactive system for users to define parameters of representations of the map (see a map representation example in Fig. 1). The demonstrated visualization method offer an opportunity to visually uncover the significant information in clinical data.
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Acknowledgements
We thank Dr. Stephen Kobourov’s group for their great work on Maps of Computer Science. Without their generosity of sharing their original codes on github, we cannot implement our ideas on the clinical data. Particularly, we are most grateful for Daniel Fried’s help in configuring and setting up the dependencies of their project on our client system, which laid a very solid foundation for our further development.
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Zhou, N., Saltz, J., Mueller, K. (2016). Maps of Human Disease: A Web-Based Framework for the Visualization of Human Disease Comorbidity and Clinical Profile Overlay. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_4
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DOI: https://doi.org/10.1007/978-3-319-41576-5_4
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