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Alternative ways to explore Clinical Data – graph visualisation

Alternative ways to explore Clinical Data – graph visualisation. Ed Cheetham, Principal Terminology Specialist. Introduction. Considerable work is already going on in making complex multi-dimensional data accessible and understandable – some of this is distinctly ‘visual’. Use of e.g.:

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Alternative ways to explore Clinical Data – graph visualisation

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  1. Alternative ways to explore Clinical Data – graph visualisation Ed Cheetham, Principal Terminology Specialist

  2. Introduction Considerable work is already going on in making complex multi-dimensional data accessible and understandable – some of this is distinctly ‘visual’ Use of e.g.: Colour Size Layout (x,y) Dynamic updating Screenshots from: Enhancing Access to UK Renal Registry Data through Innovative Online Data Visualisations. Afzal Chaudhry, Terry Feest and UK Renal Registry Interactive Geographical Maps

  3. Is SNOMED CT just the ‘data’ in these visualisations? If we want to see SNOMED CT we look at tables and browser tree controls don’t we? • Not necessarily... • Several browsers already have graphical features: • CliniClue graphical view • SNOB IHTSDO ‘standard view’ • IHTSDO workbench plugins Tend to concentrate on individual concepts. What about sets?

  4. Sets, e.g. SNOMED CT Subsets NHS Renal subset

  5. Subset subgraph - ZGRViewer GraphViz layout Dynamic highlighting Zoomable Class count limits... ZGRViewer: http://zvtm.sourceforge.net/zgrviewer.html (based on dot/GraphViz products: http://www.graphviz.org/) Use does not indicate endorsement, but extremely valuable to illustrate points discussed.

  6. Subset subgraph - Gephi Force-directed layout Dynamic labelling Node size – ‘level’ in graph Glomerulonephritis (disorder) Kidney disease (disorder) Malignant tumour of kidney (disorder) Gephi: http://gephi.org/ Use does not indicate endorsement, but extremely valuable to illustrate points discussed

  7. Subset compared to SCT corpus

  8. Areas of high and low density: Neoplasm of kidney (disorder) Infectious disorder of kidney(disorder)

  9. Refactoring greedy algorithm. From: • Looking for ‘high concept density’: • Rank each member: • Actual number [of original set members subsumed] • (Potential - actual number) + n • Threshold: • ‘Density’ must be > 1 for ‘compression’ • Can set lower threshold for ‘speculative analysis’ • n = ‘magnification factor’

  10. Refactoring greedy algorithm. To: Self+Desc Desc Self Ref’d Support

  11. Remove referenced classes to simplify: Self+Desc Desc Self Ref’d Support

  12. Application to frequency data: Synthetic (but plausible) observation data based on data from Strathclyde Renal Electronic Patient Record and UK Renal Registry data. Thanks to: Colin Geddes, Keith Simpson, Afzal Chaudhry

  13. Application to frequency data: Node size – frequency values Clear cell carcinoma of kidney(disorder) Acute pyelonephritis (disorder)

  14. ‘Speculative’ threshold: Self+Desc Desc Self Ref’d Support

  15. ‘Speculative’ threshold: Self+Desc Desc Self Ref’d Support

  16. Including ‘new nodes’ Resize post-simplification 320 -> 150 categories Self+Desc Glomerulonephritis (disorder) Desc Pseudohypoaldosteronism (disorder) Acute pyelonephritis (disorder) Self Microangiopathic hemolytic anemia (disorder) Chronic renal impairment (disorder) Ref’d Metabolic renal disease (disorder) Support

  17. Before and after ‘simplification’ • 150 categories • 90% covered by 1st 22 categories • Chronic renal impairment (disorder) [30] • Acute pyelonephritis (disorder) • Glomerulonephritis (disorder) [92] • Acute renal failure syndrome (disorder) • Proteinuria (finding) • End stage renal disease (disorder) • Diabetic renal disease (disorder) • 320 categories • 90% covered by 1st 50 categories • Chronic renal failure syndrome (disorder) • Acute pyelonephritis (disorder) • Acute renal failure syndrome (disorder) • Proteinuria (finding) • IgA nephropathy (disorder) • Chronic kidney disease stage 3 (disorder) • Diabetic renal disease (disorder)

  18. Conclusions Established visualisation techniques can be applied to SNOMED CT data [reference and instance] Exploratory and explanatory stages What does the data ‘look like’ What does that processing step ‘do’ to the data? Final views may be more ‘familiar’ (pie charts, summary tables) Competition for available axes! Need experience re. optimum contribution to the analytic process Flexible and configurable tooling Imaginative participants Standards

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