In Pharmacoepidemiology and drug safety
PURPOSE : To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA.
METHODS : This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3,400 DKA cases and 11,780 controls nested in adults with type 1 diabetes identified from Optum® de-identified Electronic Health Record dataset (2007-2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using 5-fold cross validation, and their 95% confidence intervals (CI) were established using 1,000 bootstrap samples. The importance of predictors was compared across these models.
RESULTS : In the training set, XGBoost and feedforward network yielded higher AUC values (0.89 and 0.86, respectively) than logistic regression (0.83), LASSO (0.83) and DRF (0.81). However, the AUC values were similar (0.82) among these approaches in the test set (95% CI range, 0.80-0.84). While the accuracy values >0.8 and the specificity values >0.9 for all models, the sensitivity values were only 0.4. The differences in these metrics across these models were minimal in the test set. All approaches selected some known risk factors for DKA as the top ten features. XGBoost and DRF included more laboratory measurements or vital signs compared with conventional ML approaches, while feedforward network included more social demographics.
CONCLUSIONS : In our empirical study, all ML approaches demonstrated similar performance, and identified overlapping, but different, top ten predictors. The difference in selected top predictors needs further research. This article is protected by copyright. All rights reserved.
Li Lin, Lee Chuang-Chung, Zhou Fang Liz, Molony Cliona, Doder Zoran, Zalmover Evgeny, Sharma Kristen, Juhaeri Juhaeri, Wu Chuntao
AUC, Diabetic ketoacidosis, Least absolute shrinkage and selection operator, Logistic regression, Machine learning, Prediction model