Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation

Abstract

Hemorrhagic complication (HC) is the most severe complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). This study aimed to build a machine learning (ML) prediction model and an application system for a personalized analysis of the risk of HC in patients undergoing IVT therapy. We included patients from Chongqing, Hainan and other centers, including Computed Tomography (CT) images, demographics, and other data, before the occurrence of HC. After feature engineering, a better feature subset was obtained, which was used to build a machine learning (ML) prediction model (Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB)), and then evaluated with relevant indicators. Finally, a prediction model with better performance was obtained. Based on this, an application system was built using the Flask framework. A total of 517 patients were included, of which 332 were in the training cohort, 83 were in the internal validation cohort, and 102 were in the external validation cohort. After evaluation, the performance of the XGB model is better, with an AUC of 0.9454 and ACC of 0.8554 on the internal validation cohort, and 0.9142 and ACC of 0.8431 on the external validation cohort. A total of 18 features were used to construct the model, including hemoglobin and fasting blood sugar. Furthermore, the validity of the model is demonstrated through decision curves. Subsequently, a system prototype is developed to verify the test prediction effect. The clinical decision support system (CDSS) embedded with the XGB model based on clinical data and image features can better carry out personalized analysis of the risk of HC in intravenous injection patients.

The Reasons for Physicians and Pharmacists’ Acceptance of Clinical Support Systems in Saudi Arabia

“…This finding is in line with results in prior research in general [ 31 , 84 , 85 , 86 ]. In particular, this result validates the finding of the qualitative analysis in [ 87 ]. This result can be interpreted by the fact that (70.5%) of the respondents declared that the system helped them to meet patients’ needs, and (74%) of them stated that the system allows them to accomplish more work than before.…”

“…Such a model will facilitate the measurement of users’ acceptance and behavior toward medical systems, which will shorten the required time to choose a new medical system for any organization and provide the factors of success and lessons learned. In fact, the analysis of acceptance capability is a very important step toward the successful adoption and implementation of such CDSS [ 87 ].…”

The Antecedents of Acceptance of Clinical Decision Support Systems by Physicians and Pharmacists in Saudi Arabia

“…Such a model will facilitate the measurement of users' acceptance and behavior toward medical systems, which will shorten the required time to choose a new medical system for any organization and provides the factors of success and lessons learned. In fact, the Analysis of acceptance capability is a very important step toward the successful adoption and implementation of such CDSS [87].…”