Taming EHR Data

Taming EHR Data

Jim Weatherall, PhDHead, Advanced Analytics Centre, AstraZenecaVisiting Lecturer, School of Computer Science, University of Manchester

14th World Congress on Medical & Health Informatics, August 2013, Copenhagen

Using Semantic Similarity to Reduce Dimensionality

On behalf of the authors:

Leila Kalankesh, School of Computer Science, UoMJames Weatherall, AstraZenecaThamer Ba-Dhfari, School of Computer Science, UoMIain Buchan, Institute of Population Health, UoMAndy Brass, School of Computer Science, UoM

Biometrics & Information Sciences | GMD

Introduction

Problems with mining healthcare data

J.Weatherall | August 20132

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

100s of 1000s of codes

10s of 1000s of dimensions

Large collections not easily visualised or interpreted

Biometrics & Information Sciences | GMD

Data

The Salford Integrated Record (SIR)

J.Weatherall | August 20133

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 2003-2009

Containing 136M Read code entries Selected 24K patients with chronic

conditions Containing 443K Read code entries

Methods

Semantic Similarity

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How alike are the meanings of two terms? ?

From Sanchez, J.Biomed.Inform, 2011

Measure ontological distance?

Or not?

Measure depth?

Methods

Semantic Similarity – which method?


Semantic Similarity Method

Ontological

Node-based

Edge-based

Hybrid

Corpus-based

Frequency

Context

Proximity

Combined

An ontology of methods!

Semantic similarity calculation

The Resnik measure

J.Weatherall | August 20136 Biometrics & Information Sciences | GMD

Term probability, based on frequency, including descendants and annotationsN

ccountcP

ccodesc )()(

)(1

)(log)( cPcIC 2 Log transformation, gives “Information Content”

)(),( 21Re MICAs CICccsim 3IC of “Most Informative Common Ancestor” gives similarity measure

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

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

Analysis – Step

Mapping from diagnosis space to similarity space


p1 p2 … pn

p1 sim(p1,p1) sim(p1,p2) … sim(p1,pn)

p2 sim(p2,p1) sim(p2,p2) … sim(p2,pn)

… … … … …

pn sim(pn,p1) sim(pn,p2) … sim(pn,pn)

“The Similarity Matrix”

pi = patient isim(pi,pj) = similarity score between patients i and j

1

Analysis – StepsPCA on the similarity matrix, visualisation & clustering


2 3+

Natural co-morbidity: Diabetes is a risk factor for angina due to its accelerating effect on atherosclerosis

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


New biomedical hypotheses?

Transferability of method?

Population level characterisation?

New data mining paradigms?

Exploring co-morbidity and co-treatment effects?

Thank You!

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J.Weatherall | August 2013 Biometrics & Information Sciences | GMD

Taming EHR Data

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