Medical data mining Linking diseases, drugs, and adverse reactions

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Medical data mining Linking diseases, drugs, and adverse reactions. Lars Juhl Jensen. unstructured data. structured data. Jensen et al., Nature Reviews Genetics , 2012. individual hospitals. central registries. opt-out. opt-in. Danish registries. civil registration system. CPR number. - PowerPoint PPT Presentation

Transcript of Medical data mining Linking diseases, drugs, and adverse reactions

Medical data miningLinking diseases, drugs, and adverse

reactions

Lars Juhl Jensen

unstructured data

structured data

Jensen et al., Nature Reviews Genetics, 2012

individual hospitals

central registries

opt-out

opt-in

Danish registries

civil registration system

CPR number

established in 1968

Jensen et al., Nature Reviews Genetics, 2012

national discharge registry

14 years

6.2 million patients

45 million admissions

68 million records

119 million diagnosis

ICD-10

Jensen et al., Nature Reviews Genetics, 2012

reimbursement

not research

diagnosis trajectories

naïve approach

comorbidity

Jensen et al., Nature Reviews Genetics, 2012

confounding factors

“known knowns”

gender

age

type of hospital encounter

Jensen et al., submitted, 2013

Female MaleIn

-pati

ent

Out-

pati

ent

Em

erg

ency

room

“known unknowns”

smoking

diet

“unknown unknowns”

reporting biases

disease clustering

temporal correlation

Jensen et al., submitted, 2013

diagnosis trajectories

Jensen et al., submitted, 2013

epilepsy

Jensen et al., submitted, 2013

gout

Jensen et al., submitted, 2013

electronic health records

structured data

Jensen et al., Nature Reviews Genetics, 2012

unstructured data

free text

Danish

busy doctors

psychiatric patients

delusions

text mining

named entity recognition

custom dictionaries

diseases

drugs

adverse drug events

expansion rules

orthographic variation

typos

“negative modifiers”

negations

family members

detailed disease profiles

Roque et al., PLOS Computational Biology, 2011

3262638254947

Assigned codes

Text mined codes

comorbidity

Roque et al., PLOS Computational Biology, 2011

patient stratification

Roque et al., PLOS Computational Biology, 2011

cluster characterization

Roque et al., PLOS Computational Biology, 2011

adverse drug reactions

structured data

medication

clinical narrative

possible ADRs

semi-structured data

SPCSummary of Product Characteristics

drug indications

known ADRs

temporal correlation

link drugs to ADRs

complex filtering

Eriksson et al., submitted, 2013

new ADRs

Eriksson et al., submitted, 2013

Drug substance ADE p-value

Chlordiazepoxide Nystagmus 4.0e-8

Simvastatin Personality changes

8.4e-8

Dipyridamole Visual impairment

4.4e-4

Citalopram Psychosis 8.8e-4

Bendroflumethiazide

Apoplexy 8.5e-3

ADR frequencies

Eriksson et al., submitted, 2013

heavily medicated

Eriksson et al., submitted, 2013

ADR dose dependency

Eriksson et al., submitted, 2013

ADR similarity

Eriksson et al., submitted, 2013

drug repurposing

Campillos, Kuhn et al., Science, 2008

Disease trajectoriesAnders Bøck JensenTudor OpreaPope MoseleySøren Brunak

Adverse drug reactionsRobert ErikssonThomas WergeSøren Brunak

EHR text mining

Peter Bjødstrup Jensen

Robert ErikssonHenriette SchmockFrancisco S. Roque

Anders JuulMarlene Dalgaard

Massimo AndreattaSune FrankildEva Roitmann

Thomas HansenKaren Søeby

Søren BredkjærThomas Werge

Søren Brunak

Acknowledgments

Thank you!