ABSTRACT
Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Tokyo Women Medical University East Center between 1995 and 2020 were included. We used 11 clinical variables to predict a postpartum hemorrhage defined as a blood loss of > 1000 mL. We constructed five machine learning models and a deep learning model consisting of neural networks with two layers after applying the ensemble learning of five machine learning classifiers, namely, logistic regression, a support vector machine, random forest, boosting trees, and decision tree. For an evaluation of the performance, we applied the area under the curve of the receiver operating characteristic (AUC), the accuracy, false positive rate (FPR) and false negative rate (FNR). The importance of each variable was evaluated through a comparison of the feature importance calculated using a Boosted tree. A total of 9,894 patients who underwent vaginal birth were enrolled in the study, including 188 cases (1.9%) with blood loss of > 1000 mL. The best learning model predicted postpartum hemorrhage with an AUC of 0.708, an accuracy of 0.686, FPR of 0.312, and FNR of 0.398. The analysis of the importance of the variables showed that pregnant gestation of labor, the maternal weight upon admission of labor, and the maternal weight before pregnancy were considered to be weighted factors. Machine learning model can predict postpartum hemorrhage during vaginal delivery. Further research should be conducted to analyze appropriate variables and prepare big data, such as hundreds of thousands of cases.
Subject(s)
Delivery, Obstetric/adverse effects , Machine Learning , Postpartum Hemorrhage/diagnosis , Adolescent , Adult , Area Under Curve , Deep Learning , False Positive Reactions , Female , Humans , Infant, Newborn , Labor, Obstetric , Logistic Models , Male , Middle Aged , Models, Statistical , Models, Theoretical , Neural Networks, Computer , Postpartum Hemorrhage/physiopathology , Pregnancy , ROC Curve , Reproducibility of Results , Risk , Support Vector Machine , Tokyo , Young AdultABSTRACT
Objective@#Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. @*Methods@#We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). @*Results@#The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. @*Conclusion@#The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
ABSTRACT
Objective@#Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. @*Methods@#We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). @*Results@#The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. @*Conclusion@#The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
