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’

Screenshots from:Enhancing Access to UK Renal Registry Data through Innovative Online Data Visualisations. Afzal Chaudhry, Terry FeestandUK Renal Registry Interactive Geographical Maps

Use of e.g.:ColourSizeLayout (x,y)Dynamic updating

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?

Sets, e.g. SNOMED CT Subsets

NHS Renal subset

Subset subgraph - ZGRViewer

Zoomable

Dynamic highlighting

GraphViz layout

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.

Class count limits...

Subset subgraph - Gephi

Node size – ‘level’ in graph

Force-directed layout

Dynamic labelling

Gephi: http://gephi.org/Use does not indicate endorsement, but extremely valuable to illustrate points discussed

Glomerulonephritis (disorder)

Kidney disease (disorder)

Malignant tumour of kidney (disorder)

Subset compared to SCT corpus

Areas of high and low density:

Neoplasm of kidney (disorder)

Infectious disorder of kidney(disorder)

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’

Refactoring greedy algorithm. To:

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Remove referenced classes to simplify:

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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

Application to frequency data:Node size – frequency values

Acute pyelonephritis (disorder)

Clear cell carcinoma of kidney(disorder)

‘Speculative’ threshold:

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‘Speculative’ threshold:

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Including ‘new nodes’

Microangiopathic hemolytic anemia (disorder)

Pseudohypoaldosteronism (disorder)

Metabolic renal disease (disorder)

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Acute pyelonephritis (disorder)

Chronic renal impairment (disorder)

Glomerulonephritis (disorder)

Resize post-simplification

320 -> 150 categories

Before and after ‘simplification’

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320 categories90% 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)

150 categories90% 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)

Conclusions

• Established visualisation techniques can be applied to SNOMED CT data [reference and instance]

Exploratory and explanatory stagesWhat 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 toolingImaginative participantsStandards