Medical data mining
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Medical data mining Lars Juhl Jensen
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Transcript of Medical data mining
Medical data mining
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
computer
as smart as a dog
teach it specific tricks
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
AcknowledgmentsDisease 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
Thank you!
