# AnamneVis: a framework for the visualization of patient history and medical diagnostics chains
Zhang et al. present AnamneVis , an information visualization tool for EHR that comprehensibly organizes EHR documents via the concept of Five W’s (who, when, what, where, why, and also how).
As identified by the authors, the inefficient and fragmented display of patient information is a major cause that contributes towards the low acceptance of EHR in clinical practice, which lags far behind its expectation and potential.
AnamneVis first applies NLP for extracting structured information and relationships from EHRs, and uses International Classification of Diseases (ICD) as the medical standard for mapping diseases and symptoms.
Figure 1 shows an overview of AnamneVis, the authors use the sunburst chart as the base and encode each node with additional information. The first layer corresponds to the highest code hierarchy level. The second layer shows detailed sub-categories. The third layer contains symptoms, procedures or diagnosis. Users can interactively expand and collapse nodes for exploration.
Figure 1: Hierarchy-centric layout (left), Patient-centric layout (middle), Display with ICD code name/label overlay (right).
Figure 2 shows the sequential display that embodies the medical diagnostic flow using a sankey diagram.
Figure 2: Sequential display for diagnostic chain.
- Data source: unknown
- Size: unknown
- Spatial dimensionality: 2D
- Temporal dimensionality: static
- Type: multivariate
- Libraries used: unknown
- Information Visualization of EHR
Zhang, Z., Ahmed, F., Ramakrishnan, A. M. I. V, Zhao, R., Viccellio, A., & Mueller, K. (2011). AnamneVis: a framework for the visualization of patient history and medical diagnostics chains. IEEE VAHC Workshop, (January), 1–4. ↩︎
Plaisant C, Milash B, Rose A, et al. LifeLines: visualizing personal histories. SIGCHI Conference on Human Factors in Computing Systems Proceedings; 1996:221–227 ↩︎
Plaisant C, Mushlin R, Snyder A, et al. LifeLines: using visualization to enhance navigation and analysis of patient records. AMIA Symposium Proceedings; 1998:76–80 ↩︎
Bade, R., Schlechtweg, S., & Miksch, S. (2004). Connecting time-oriented data and information to a coherent interactive visualization. Conference on Human Factors in Computing Systems - Proceedings. ↩︎
Aigner, W., Miksch, S., Muller, W., Schumann, H., & Tominski, C. (2008). Visual Methods for Analyzing Time-Oriented Data. IEEE Transactions on Visualization and Computer Graphics, 14(1), 47–60. https://doi.org/10.1109/TVCG.2007.70415. ↩︎
Wang, T. D., Plaisant, C., Quinn, A. J., Stanchak, R., Murphy, S., & Shneiderman, B. (2008). Aligning temporal data by sentinel events. Proceeding of the Twenty-Sixth Annual CHI Conference on Human Factors in Computing Systems - CHI ’08, 457. https://doi.org/10.1145/1357054.1357129 ↩︎