Transcript Slide 1

Drug Exposure Side Effects
from Mining Pregnancy Data1
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Difficulties in finding side effects:
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Small number of patients suffer side effect
Sensitive to the drug exposure time
Exposure to sequence of multiple drugs
Statistical analysis
Data mining
Infeasible to test all potential
hypotheses for large number
of attributes
Testing hypotheses with small
sample size has limited
statistical power
 No hypothesis, mine association in large dataset with
multiple temporal attributes
 Can generate association rules
independent of the sample size
 Derive rules with temporal
information of drug exposure
Discover Side Effects from MFI
Data Mining
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Hierarchically organize
rules into trees
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View general rules and
then extend to specific
rules
Use spreadsheet to
present the rule trees
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Easy to sort, filter or
extend the rule trees to
search for the interesting
rules
1) In general, patients have preterm birth (sup=0.0454,
conf=0.0454)
2) If exposed to cita in the 1st trimester, then
preterm birth (sup=0.0016, conf=0.0761)
6) If exposed to cita in the 1st trimester and
drink alcohol, then preterm birth (sup=0.0011,
conf=0.132)
3) If exposed to cita in the 2nd trimester, then
preterm birth (sup=0.0013, conf=0.1714)
7) If exposed to cita in the 2nd trimester and
drink alcohol, then preterm birth (sup=0.0011,
conf=0.417)
4) If exposed to cita in the 3rd trimester, then
preterm birth (sup=0.0011, conf=0.1786)
8) If exposed to cita in the 3rd trimester and
drink alcohol, then preterm birth (sup=0.0009,
conf=0.364)
5) If no exposure to cita, then preterm birth
(sup=0.0433, conf=0.0444)
A part of the rule hierarchy for the exposure to the
antidepressant citalopram and alcohol at different time
period of pregnancy with preterm birth
New Findings
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1Yu
Based on the large-scale
Danish National Birth Cohort
(DNBC) dataset
Finding: combined exposure to
citalopram and alcohol in
pregnancy is associated with
an increased risk of preterm
birth
Not initially discovered by
epidemiology study due to the
large number of combinations
among all the attributes and
their values
1) In general, patients have preterm birth (sup=0.0454,
conf=0.0454)
2) If exposed to cita in the 1st trimester, then
preterm birth (sup=0.0016, conf=0.0761)
6) If exposed to cita in the 1st trimester and
drink alcohol, then preterm birth (sup=0.0011,
conf=0.132)
3) If exposed to cita in the 2nd trimester, then
preterm birth (sup=0.0013, conf=0.1714)
7) If exposed to cita in the 2nd trimester and
drink alcohol, then preterm birth (sup=0.0011,
conf=0.417)
4) If exposed to cita in the 3rd trimester, then
preterm birth (sup=0.0011, conf=0.1786)
8) If exposed to cita in the 3rd trimester and
drink alcohol, then preterm birth (sup=0.0009,
conf=0.364)
5) If no exposure to cita, then preterm birth
(sup=0.0433, conf=0.0444)
Chen, Lars Henning Pedersen, Wesley W. Chu and Jorn Olsen. "Drug Exposure Side Effects
from Mining Pregnancy Data" In SIGKDD Explorations (Volume 9, Issue 1), Special Issue on
Data Mining for Health Informatics, June 2007