Novel electronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms

OBJECTIVE: To predict risk of pre-eclampsia (PE) in women using machine learning (ML) algorithms, based on electronic health records (EHR) collected at the early second trimester. STUDY DESIGN: A total of 3759 cases of pregnancy who received antenatal care at Xinhua hospital Chongming branch Affiliated to Shanghai Jiaotong University were included in this retrospective EHR-based study. Thirty-eight candidate clinical parameters routinely available at the first visit in antenatal care were collected by manual chart review. Logistic regression (LR), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to construct the prediction model. Features that contributed to the model predictions were identified using XGBoost. OUTCOME MEASURES: The performance of ML models to predict women at risk of PE was quantified in terms of accuracy, precision, recall, false negative score, f1ₛcore, brier score and the area under the receiver operating curve (auROC). RESULTS: The XGboost model had the best prediction performance (accuracy = 0. 920, precision = 0. 447, recall = 0. 789, f1ₛcore = 0. 571, auROC = 0. 955). The most predictive feature of PE development was fasting plasma glucose, followed by mean blood pressure and body mass index. An easy-to-use model that a patient could answer independently still enabled accurate prediction, with auROC of 0. 83. CONCLUSION: risk of PE development can be predicted with excellent discriminative ability using ML algorithms based on EHR collected at the early second trimester. Future studies are needed to assess the real-world clinical utility of the model.