1. Drug Exposure Side Effects from Mining Pregnancy Data 1 Difficulties in finding side effects: Small number of patients suffer side effect Sensitive to the drug exposure time Exposure to sequence of multiple drugs Statistical analysis Infeasible to test all potential hypotheses for large number of attributes Testing hypotheses with small sample size has limited statistical power Data mining  No hypothesis, mine associa- tion in large dataset with multiple temporal attributes  Can generate association rules independent of the sample size  Derive rules with temporal information of drug exposure

2. Discover Side Effects from MFI Data Mining Hierarchically organize rules into trees View general rules and then extend to specific rules Use spreadsheet to present the rule trees Easy to sort, filter or extend the rule trees to search for the interesting rules 2) If exposed to cita in the 1 st trimester, then preterm birth (sup=0.0016, conf=0.0761) 6) If exposed to cita in the 1 st trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.132) 7) If exposed to cita in the 2 nd trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.417) 3) If exposed to cita in the 2 nd trimester, then preterm birth (sup=0.0013, conf=0.1714) 4) If exposed to cita in the 3 rd trimester, then preterm birth (sup=0.0011, conf=0.1786) A part of the rule hierarchy for the exposure to the antidepressant citalopram and alcohol at different time period of pregnancy with preterm birth 8) If exposed to cita in the 3 rd trimester and drink alcohol, then preterm birth (sup=0.0009, conf=0.364) 1) In general, patients have preterm birth (sup=0.0454, conf=0.0454) 5) If no exposure to cita, then preterm birth (sup=0.0433, conf=0.0444)

3. New Findings 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 2) If exposed to cita in the 1 st trimester, then preterm birth (sup=0.0016, conf=0.0761) 6) If exposed to cita in the 1 st trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.132) 7) If exposed to cita in the 2 nd trimester and drink alcohol, then preterm birth (sup=0.0011, conf=0.417) 3) If exposed to cita in the 2 nd trimester, then preterm birth (sup=0.0013, conf=0.1714) 4) If exposed to cita in the 3 rd trimester, then preterm birth (sup=0.0011, conf=0.1786) 8) If exposed to cita in the 3 rd trimester and drink alcohol, then preterm birth (sup=0.0009, conf=0.364) 1) In general, patients have preterm birth (sup=0.0454, conf=0.0454) 5) If no exposure to cita, then preterm birth (sup=0.0433, conf=0.0444) 1 Yu 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