1. Taming EHR Data Using Semantic Similarity to Reduce Dimensionality
Jim Weatherall, PhD
Head, Advanced Analytics Centre, AstraZeneca
Visiting Lecturer, School of Computer Science, University of Manchester
14th World Congress on Medical & Health Informatics, August 2013, Copenhagen
On behalf of the authors:
Leila Kalankesh, School of Computer Science, UoM
James Weatherall, AstraZeneca
Thamer Ba-Dhfari, School of Computer Science, UoM
Iain Buchan, Institute of Population Health, UoM
Andy Brass, School of Computer Science, UoM
Brief: Talk for 14 mins, then 2 mins questions, then 2 mins switchover

2. Problems with mining healthcare data
Introduction
Problems with mining healthcare data
Large collections not easily visualised or interpreted
Read Code
Rubric
C10F.
Type II Diabetes Mellitus,
1372.
Trivial smoker < 1 cig/day
bd3j.
Prescription of “Atenolol 25mg tablets”
G20.
Essential hypertension
2469.
Measurement of Diastolic Blood Pressure
246A.
Assessment of Diastolic Blood Pressure
Research not primary purpose for collection
10s of 1000s of dimensions
100s of 1000s of codes
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

3. The Salford Integrated Record (SIR)
Data
The Salford Integrated Record (SIR)
Population ~220,000
Integrated primary and secondary care information
Individual Read Code entries captured in primary care information systems
Codes for diagnosis
Codes for procedures
All clinical transactions in primary care and some in secondary care
Data extract for this analysis based on:
GP data in date range
Containing 136M Read code entries
Selected 24K patients with chronic conditions
Containing 443K Read code entries
Type 1 DM
Type 2 DM MI
Angina
Stroke / CVA
TIA
CKD
Liver disease
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

4. Measure ontological distance?
Methods
Semantic Similarity ?
How alike are the meanings of two terms?
Measure depth?
Or not?
Measure ontological distance?
Also:
Relative depth
Multiple inheritance
Lateral connections
These are edge-based, what about node-based
All this is ontology-based, what about corpus based?
J.Weatherall | August 2013
From Sanchez, J.Biomed.Inform, 2011
Biometrics & Information Sciences | GMD

5. Semantic Similarity Method
Methods
Semantic Similarity – which method?
An ontology of methods!
Semantic Similarity Method
Ontological
Node-based
Edge-based
Hybrid
Corpus-based
Frequency
Context
Proximity
Combined
Corpus-based is realistic and grounded in data, but relies on having a large enough corpus, and can also be biased & computationally expensive
Ontology-based is often computationally lighter, and rooted in the knowledge domain of interest, but can lack realism due to lack of use of real data
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

6. Semantic similarity calculation
The Resnik measure
Term probability, based on frequency, including descendants and annotations 1 2
Log transformation, gives “Information Content” 3
IC of “Most Informative Common Ancestor” gives similarity measure
N = total number of all codes in data set
c ϵ codes(c) = count of all instances of code, as well as annotations and descendents
P. Resnik, “Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language”, J Artif Intell Res, 1999
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

7. Stepwise approach to dimensionality reduction
Analysis Plan
Stepwise approach to dimensionality reduction
Map patient records from diagnosis space into a similarity space 1
Map patient records into a low-dimensional vector space via PCA 2
Project patient records onto low-dimensional vector space and cluster patients by similarity 3
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

8. “The Similarity Matrix”
Analysis – Step 1
Mapping from diagnosis space to similarity space p1 p2 … pn
sim(p1,p1)
sim(p1,p2)
sim(p1,pn)
sim(p2,p1)
sim(p2,p2)
sim(p2,pn)
sim(pn,p1)
sim(pn,p2)
sim(pn,pn)
“The Similarity Matrix”
pi = patient i
sim(pi,pj) = similarity score between patients i and j
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

9. Analysis – Steps 2 + 3
PCA on the similarity matrix, visualisation & clustering
Natural co-morbidity: Diabetes is a risk factor for angina due to its accelerating effect on atherosclerosis
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

10. Discussion & Conclusion
Review & Outlook
Patients with similar diagnosis codes are grouped together
Therefore, the semantic similarity technique works, to some degree
Therefore, this is a viable route to dimensionality reduction in complex healthcare data sets
Exploring co-morbidity and co-treatment effects?
New biomedical hypotheses?
Transferability of method?
Population level characterisation?
New data mining paradigms?
J.Weatherall | August 2013
Biometrics & Information Sciences | GMD

11. Thank You!

12. Confidentiality Notice
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J.Weatherall | August 2013
Biometrics & Information Sciences | GMD