Information visualisation in clinical Odontology: multidimensional analysis and interactive data exploration

In 1995, the MedView project, based on a co-operation between computing science and clinical medicine was initiated. The overall goal of the project was to develop models, methods and tools to support clinicians in their daily diagnostic work. As part of MedView, two information visualisation tools were developed and tested as solutions to the problem of visualising clinical experience derived from large amounts of clinical data. The first tool (The Cube) was based on the idea of dynamic three-dimensional (3D) parallel diagrams, an idea similar to the notion of 3D parallel co-ordinates. The Cube was developed to enhance the clinician's ability to intelligibly analyse existing patient material and to allow for pattern recognition and statistical analysis. The second tool (SimVis) was based on a similarity assessment-based interaction model for exploring data, and was designed to help clinicians to classify and cluster clinical examination data. User interaction was supported by 3D visualisation of clusters and similarity measures. Both tools were tested on a knowledge base containing about 1500 examinations obtained from different clinics. Clinical practice indicated that the basic ideas are conceptually appealing to the involved clinicians as the tools can be used for generating and testing of hypotheses.

[1]  Ramana Rao,et al.  The Hyperbolic Browser: A Focus + Context Technique for Visualizing Large Hierarchies , 1996, J. Vis. Lang. Comput..

[2]  Chang-Sung Jeong,et al.  Reconfigurable disc trees for visualizing large hierarchical information space , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[3]  Barry Smyth,et al.  Advances in Case-Based Reasoning , 1996, Lecture Notes in Computer Science.

[4]  Alfred Inselberg,et al.  The automated multidimensional detective , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[5]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[6]  Graham J. Wills,et al.  An interactive view for hierarchical clustering , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[7]  Peter A. Lachenbruch,et al.  Classification: Methods for the Exploratory Analythi of Multivariate Data , 1982 .

[8]  Raúl E. Valdés-Pérez,et al.  Principles of Human Computer Collaboration for Knowledge Discovery in Science , 1999, Artif. Intell..

[9]  Ivan Herman,et al.  Graph Visualization and Navigation in Information Visualization: A Survey , 2000, IEEE Trans. Vis. Comput. Graph..

[10]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[11]  Evans Cd,et al.  A case-based assistant for diagnosis and analysis of dysmorphic syndromes , 1995 .

[12]  Alan Keahey The generalized detail in-context problem , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[13]  Michael Eisenberg,et al.  The thin glass line: designing interfaces to algorithms , 1996, CHI.

[14]  Mark Green,et al.  The Information Cube: Using Transparency in 3D Information Visualization , 1993 .

[15]  Jock D. Mackinlay,et al.  The perspective wall: detail and context smoothly integrated , 1991, CHI.

[16]  Göran Falkman,et al.  Similarity Measures for Structured Representations: A Definitional Approach , 2000, EWCBR.

[17]  Steven K. Feiner,et al.  Worlds within worlds: metaphors for exploring n-dimensional virtual worlds , 1990, UIST '90.

[18]  D. Gentner Structure‐Mapping: A Theoretical Framework for Analogy* , 1983 .

[19]  F. A. da Veiga,et al.  Structure discovery in medical databases: a conceptual clustering approach , 1996, Artif. Intell. Medicine.

[20]  B. Schneirdeman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[21]  Edward J. Wegman,et al.  The Grand Tour in k-Dimensions , 1992 .

[22]  Lars Hallnäs Partial inductive definitions , 1991 .

[23]  E. Wegman Hyperdimensional Data Analysis Using Parallel Coordinates , 1990 .

[24]  D. Hofstadter Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, Douglas Hofstadter. 1994. Basic Books, New York, NY. 512 pages. ISBN: 0-465-05154-5. $30.00 , 1995 .

[25]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[26]  W Giere,et al.  Multi-dimensional Visualisation of Laboratory Findings and Functional Test Results for Analysing the Clinical Course of Disease in Medicine , 1995, Methods of Information in Medicine.

[27]  A. D. Gordon,et al.  Classification : Methods for the Exploratory Analysis of Multivariate Data , 1981 .

[28]  Vic Barnett,et al.  Interpreting multivariate data , 1982 .

[29]  Ivan Herman,et al.  Graph Visualisation and Navigation in Information Visualisation , 1999, Eurographics.

[30]  Rick Kazman,et al.  Research report. Interacting with huge hierarchies: beyond cone trees , 1995, Proceedings of Visualization 1995 Conference.

[31]  Stefan Berchtold,et al.  Similarity clustering of dimensions for an enhanced visualization of multidimensional data , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[32]  Peter Eades,et al.  A Fully Animated Interactive System for Clustering and Navigating Huge Graphs , 1998, GD.

[33]  Francesco Ricci,et al.  CBET: Acase Base Exploration Tool , 1997, AI*IA.

[34]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[35]  Jock D. Mackinlay,et al.  Information visualization using 3D interactive animation , 1993, CACM.

[36]  A. Tversky Features of Similarity , 1977 .

[37]  Alfred Inselberg Visual Data Mining with Parallel Coordinates , 1998 .

[38]  Matthew O. Ward,et al.  Hierarchical parallel coordinates for exploration of large datasets , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[39]  Thomas A. DeFanti,et al.  Visualization in Scientific Computing-A Synopsis , 1987, IEEE Computer Graphics and Applications.

[40]  Saul Greenberg,et al.  Navigating hierarchically clustered networks through fisheye and full-zoom methods , 1996, TCHI.

[41]  A. D. Gordon,et al.  Interpreting multivariate data , 1982 .

[42]  Ian D. Watson,et al.  An Introduction to Case-Based Reasoning , 1995, UK Workshop on Case-Based Reasoning.

[43]  Daniel Asimov,et al.  The grand tour: a tool for viewing multidimensional data , 1985 .

[44]  Matthew O. Ward,et al.  Navigating hierarchies with structure-based brushes , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[45]  Ben Shneiderman,et al.  Tree visualization with tree-maps: 2-d space-filling approach , 1992, TOGS.

[46]  Hing-Yan Lee,et al.  Visualization Support for Data Mining , 1996, IEEE Expert.

[47]  T. A. Keahey Visualization of high-dimensional clusters using nonlinear magnification , 1999, Electronic Imaging.

[48]  B. H. McCormick,et al.  Visualization in scientific computing , 1995 .

[49]  Göran Falkman SimVis: an interaction model for exploring clinical data , 2000, CHI Extended Abstracts.

[50]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .