A computerized algorithm to capture patient's past preeclampsia and eclampsia history from prenatal clinical notes.

Prenatal clinical notes in electronic medical records contain a wealth of information on pregnancy complications and outcomes. Extracting this critical information provides a unique opportunity for risk assessment to identify at-risk patients who may benefit from early monitoring and intervention. We developed and validated a rule-based computerized algorithm called PregHisEx to characterize past obstetrical history (preeclampsia/eclampsia) by mining prenatal clinical notes for women delivered in 2012 within a large healthcare maintenance organization. The algorithm successfully identified cases with past history of preeclampsia/eclampsia: 2984 definite and 479 probable cases at sentence level; 2419 definite and 348 probable cases at note level; and 762 definite and 82 probable cases at pregnancy episode level. Validation conducted on a random sample of 200 notes using PregHisEx yielded 88.0 percent sensitivity, 98.9 percent specificity, 91.7 percent positive predictive value, 98.3 percent negative predictive value, and F-score of 0.90. The high-performing PregHisEx can be applied for other prenatal conditions.