NetBioSIG2012 annabauermehren
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Network analysis of unstructured EHR data for compara8ve effec8veness research Anna Bauer)Mehren ShahLab, Stanford Center for Biomedical Informa:cs
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Mining of electronic health records (EHRs) has recently gained importance. However, most efforts are restricted to analyzing drugs, diseases and their associations. In biomedical research, network analysis has provided the conceptual framework to interpret protein-protein interactions or gene-disease association networks via large-scale network maps. We analyze associations between drugs, diseases, devices and procedures mined from EHRs using network analysis to extract hidden “modules of care” for hypothesis generation. In particular, we annotated the textual notes of the EHRs of one million patients in the Stanford Clinical Data Warehouse with disease, drug, procedure and device terms using ontologies such as SNOMED-CT or RxNorm. We then used standard co-occurrence statistics to establish associations between these clinical concepts and to construct networks. Hidden modules of care - clusters of diseases, drugs, procedures, devices – useful for hypothesis generation are extracted through network analysis approaches and visualized using Cytoscape. We present a study for comparative effectiveness of Cilostazol vs. a control group in peripheral artery disease (PAD) patients (see Figure 1) and compare our results derived from the network analysis against standard methods such as regression analysis. We believe that network analysis allows us to uncover hidden (“latent”) modules of care not detected through standard approaches, which do not account for the connectivity of the clinical events and entities.
Transcript of NetBioSIG2012 annabauermehren
Network(analysis(of(unstructured(EHR(data(for(compara8ve(effec8veness(research(
Anna$Bauer)Mehren$ShahLab,$Stanford$Center$for$Biomedical$Informa:cs$
Background(>(PAD(
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• Peripheral$artery$disease$(PAD):$obstruc:on$of$infra)renal$abdominal$aorta$and$lower$extremity$arteries$$
• Symptoms:$IntermiEent$claudica:on$$
• Risk$factors:$Smoking,$Diabetes,$Obesity,$Hypertension,$Age$
• Treatments:$Lifestyle$changes,$Surgical$and$endovascular$interven:ons,$Pharmacotherapy$$
!$Cilostazol$
Background(>(Cilostazol(• Reversible$selec:ve$inhibitor$of$phosphodiesterase$(PDE)$type$III$
• Long)term$oral$milrinone$therapy$associated$with$life)threatening$cardiovascular$events$in$conges:ve$heart$failure$(CHF)$pa:ents$
$
$$$$$Black$box$warning:$$$
$$
$$$$$Hypothesis:$
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Cilostazol is contraindicated in patients with CHF
Cilostazol is not associated with increased risk of major adverse cardiovascular events (MACE)
• 1.6$million$pa:ents$
• 15$million$visits$
• 25$million$coded$ICD9$diagnoses$
• 9.5$million$clinical$free$text$notes$• pathology$• radiology$• transcrip:on$reports$
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NCBO(STRIDE(• We$create$and$maintain$a$
library$of$more$than$300$biomedical$ontologies$
• We$build$tools$and$web$services$to$enable$the$use$of$ontologies$
• We$use$ontologies$for$annota:ng$EHRs$to$allow$clinically$relevant$studies$
Networks(for(compara8ve(effec8veness(
• Networks$have$been$successfully$applied$in$biology$
• Can$we$use$networks$for$clinical$studies?$
• We$want$to$use$networks$for$• Cohort$building$
• Analysis$of$clinical$outcome$$
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Roque et al, PLoS Comput Biol. 2011; 7(8): e1002141.
Term recognition tool NCBO Annotator
NegEx Patterns
• Negation detection • FH detection
Patient feature matrix
Sel
ect c
ohor
t of
Inte
rest
Drugs
BioPortal – knowledge graph
Creating clean lexicons Annotation Workflow
Furthe
r$Analysis$
Text clinical note
Terms Recognized
Negation, FH detected
From(clinical(notes(to(pa8ent(feature(matrix(
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Diseases
Devices Procedures
Term$–$1$:$:$Term$–$n$
Dimension(reduc8on(using(ontologies(
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Follow up time in peripheral artery disease patients
follow up time in 30 day intervals
Freq
uenc
y
0 1000 2000 3000 4000 5000 6000
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Cohort(building(–(restrict(by(follow(up(8me(
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5757 PAD patients
t
Patient timeline
Follow up time
PAD
tPAD
Last note
tlast
Cohort(building(–(set(8me(
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t
Patient timeline
PAD
t PAD
Last note
t last
CIL
t CIL= t0
t
PAD
t PAD
22 days
t 0
before after
Median = 22 days
Cilostazol patient
Other PAD patient
Last note
t last
Matching Outcome
Matching(
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1. Choose$variables$for$matching$
2. Compute$PS$based$on$variables$(logis:c$regression)$
3. Nearest$neighbor$matching$(1:1)$
1 0 1
0 0 1
1 1 0
1 0 1
0 0 1
0 0 1
0 0 1
1 1 0
0 0 1
0 0 1
1 1 1
0 0 1
1 1 1
0 1 0
0 0 1
0 1 0
0 0 0
0 1 1
0 0 1
0 1 1
1 1 1
0 0 1
1 0 1
1 1 1
Drug$classes$ Diseases$ Devices$ Procedures$ Demographics$
J(A,B) =1−A∩BA∪B
0 0.6 0.8 0.6 0.5 0.8
0 0.8 0.9 0.8 0.3
0 0.7 0.9 0.9
0 0.7 0.8
0 0.8
0
Pa:e
nts$
Pa:ents$
5776(
5776(Nearest(neighbor(Matching((1:1)(
1 0 1
0 0 1
1 1 0
1 0 1
0 0 1
0 0 1
0 0 1
1 1 0
0 0 1
0 0 1
1 1 1
0 0 1 446(
1159(
Cilostazol(
Control(
Concepts$
0 0 1
0 1 1
1 1 1
0 0 1
1 0 1
1 1 1
Pa8ent>pa8ent(similarity(network( Propensity>score(matching(
5776(
1159(
Pa:e
nts$
Pa:e
nts$
1 0 1
0 0 1
1 1 0
1 0 1
0 0 1
0 0 1
0 0 1
1 1 0
0 0 1
0 0 1
1 1 1
0 0 1
446(
17(Concepts$
Pa:e
nts$
Matching(>(comparison(
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!!!!!
Variable Before Matching Patient-patient similarity network
Propensity score matching
Treatment
(n= 223) Control (n= 5534)
p-value Control (n= 223)
p-value Control (n= 223)
p-value
Demographics Age (at indication onset), mean (sd) 71.22
(11.02) 70.43
(12.46) 0.295 72.05
(10.62) 0.41196 70.87
(11.51) 0.74704
Gender (female), n (%) 37.22 45.94 0.0090723 36.65 0.84432 35.87 0.76777 Race , (%)
Asian 8.52 7.41 0.56047 6.33 0.36671 10.31 0.50525 Black 2.69 3.71 0.36423 3.61 0.588 0.90 0.15684 Unknown 22.87 26.17 0.25365 22.17 0.82063 20.63 0.57404 White 65.47 62.22 0.31854 67.87 0.54642 67.27 0.68645
Comorbidities Coronary artery disease, n (%) 5.38 6.47 0.48345 4.98 0.83089 6.28 0.69516 Congestive heart failure, n (%) 25.56 22.84 0.36255 20.36 0.21361 30.49 0.36255 Hypertension, n (%) 10.76 11.31 0.79597 9.50 0.75135 10.31 0.87893 Co-prescriptions Beta blocking agents, n (%) 75.34 60.77 1.6611e-06 69.68 0.20252 74.89 0.90693 ACE inhibitors, plain, n (%) 78.03 69.57 0.0032697 67.87 0.013595 78.92 0.81386 Platelet aggregation inhibitors excl.
heparin, n (%) 91.93 79.00 8.1983e-11 89.59 0.41347 95.51 0.072929
Vasodilators, n (%) 32.29 26.36 0.064893 31.67 0.83902 37.22 0.28753 History of Cardiac arrhythmia, n (%) 32.29 32.17 0.96957 23.08 0.03383 33.18 0.84006 Stroke, n (%) 17.94 18.31 0.88877 15.84 0.61129 21.52 0.33903 Myocardial infarction, n (%) 17.94 15.87 0.43015 13.58 0.23919 19.73 0.63765 Vascular surgical procedures, n (%) 74.44 47.71 < 2.22e-16 65.61 0.048937 74.44 1 Bypass surgery, n (%)
41.70 26.56 1.0506e-05 36.20 0.24269 40.36 0.75073
Cohort(building(–(set(8me(
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t
Patient timeline
PAD
t PAD
Last note
t last
CIL
t CIL= t0
t
PAD
t PAD
22 days
t 0
before after
Median = 22 days
Cilostazol patient
Other PAD patient
Last note
t last
Cohort matching
Matching Outcome
Outcome(comparison(
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$$
$
$$
Concept>concept(network( Dispropor8onality(analysis(
- 0.5 2.5 4 10
- 1.3 2 0
- 0 0
- 1
-
- 0.5 2.5 4 10
- 1.3 2 0
- 0 0
- 1
-
control
cilostazol ascid1,cid2 = log(cofreqcid1,cid2
freq1cid1 * freq2cid2n
)
!!!!!!!!
"
#
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%
&
0321
3303231
2232021
1131110
dddd
d
d
d
ββββ
ββββ
ββββ
ββββ
Compute edges
Logistic regression
1 0 0 1 1
0 1 1 0 1
0 1 0 0 1
0 1 1 0 0
1 0 1 0 0
1 1 1 1 0
zolpidem
pravastatin
nifedipine
congestive heart failure
insulin glargine
cilostazol
transplantation
trimethoprim
bypass
doppler studies
surgical revision
atherectomy
bypass graftvascular surgical
procedures
ultrasound imaging
testosterone
fentanyl
amoxicillin
heart failure
coronary angiography
pantoprazole
cephalexin
hydralazine
amiodarone
obesity
wheelchair
diazepam
pneumonia
tacrolimus
sulfamethoxazole
temazepam
decompressive incision
fluoroscopic angiography heart
transplantation
revascularization
diagnostic imaging
vascular diseases
ramipril
angioplasty
doppler ultrasonography
cane
carotid endarterectomy
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5
Ventricular tachicardia
Ventricular fibrillation
Vasulcar surgical procedures
Stroke
Myocardial infarction
Electric countershock
Death
Coronary stents
Cardiac arrythmia
Bypass surgery
Atrial fibrillation
Odds ratios − cilostazol vs. control
similaritypsm
zolpidem
pravastatin
nifedipine
congestive heart failure
insulin glargine
cilostazol
transplantation
trimethoprim
bypass
doppler studies
surgical revision
atherectomy
bypass graftvascular surgical
procedures
ultrasound imaging
testosterone
fentanyl
amoxicillin
heart failure
coronary angiography
pantoprazole
cephalexin
hydralazine
amiodarone
obesity
wheelchair
diazepam
pneumonia
tacrolimus
sulfamethoxazole
temazepam
decompressive incision
fluoroscopic angiography heart
transplantation
revascularization
diagnostic imaging
vascular diseases
ramipril
angioplasty
doppler ultrasonography
cane
carotid endarterectomy
Concept>concept(network(
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Vascular surgical procedures
Heart transplantation
Heart failure
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5
Ventricular tachicardia
Ventricular fibrillation
Vasulcar surgical procedures
Stroke
Myocardial infarction
Electric countershock
Death
Coronary stents
Cardiac arrythmia
Bypass surgery
Atrial fibrillation
Odds ratios − cilostazol vs. control
similaritypsm
Dispropor8onality(analysis(
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Conclusions(
• Technical$• Pa:ent)pa:ent$network$based$on$similarity$in$annota:ons$can$be$used$for$cohort$building$
• Concept)concept$network$based$on$co)occurrence$in$clinical$notes$can$be$used$for$analyzing$clinical$outcome$
• Medical$• No$significant$differences$in$MACE$comparing$cilostazol$vs.$control$$
$
$
7/17/12 NetBio SIG, 2012 18
zolpidem
pravastatin
nifedipine
congestive heart failure
insulin glargine
cilostazol
transplantation
trimethoprim
bypass
doppler studies
surgical revision
atherectomy
bypass graftvascular surgical
procedures
ultrasound imaging
testosterone
fentanyl
amoxicillin
heart failure
coronary angiography
pantoprazole
cephalexin
hydralazine
amiodarone
obesity
wheelchair
diazepam
pneumonia
tacrolimus
sulfamethoxazole
temazepam
decompressive incision
fluoroscopic angiography heart
transplantation
revascularization
diagnostic imaging
vascular diseases
ramipril
angioplasty
doppler ultrasonography
cane
carotid endarterectomy
Cilostazol is contraindicated in patients with CHF
Cilostazol is not associated with increased risk of major adverse cardiovascular events (MACE)
Future(work(
• Technical$• Improve$aggrega:on$of$concepts$
• Might$improve$cohort$building$• Useful$for$outcome$analysis$based$on$networks$
• Medical$• Repeat$study$in$different$hospital$• Profile$PAD$pa:ents$using$all$annota:ons$using$concept)concept$networks$for$hypothesis$genera:on$
7/17/12 NetBio SIG, 2012 19
sulfamethoxazole
carotid endarterectomy
coronary angiography
zolpidemtrimethoprim
transplantation
pantoprazoleinsulin glargineobesity
amiodarone pravastatin
hydralazinepneumonia
wheelchair
cilostazol
angioplasty
congestive heart failure
heart failure
sulfamethoxazole
carotid endarterectomy
coronary angiography
zolpidemtrimethoprim
transplantation
pantoprazoleinsulin glargineobesity
amiodarone pravastatin
hydralazinepneumonia
wheelchair
cilostazol
angioplasty
heart failure
Acknowledgements(
• ShahLab$• Nigam$Shah$• Paea$LePendu$• Rave$Harpaz$• Srinivasan$Iyer$• Amogh$Vasekar$• Kenneth$Jung$
• Medical$collaborator$• Nicholas$Leeper$• John$Cooke$
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• NCBO$Team$• Mark$Musen$• NIH$funding$
$• STRIDE$Team$
• Tanya$Podchiyska$• Todd$Ferris$$
