Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost

• URL: http://arxiv.org/abs/2411.18063v1
• Date: Wed, 27 Nov 2024 05:15:55 GMT
• Title: Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost
• Authors: Yalcin Tur, Vedat Cicek, Tufan Cinar, Elif Keles, Bradlay D. Allen, Hatice Savas, Gorkem Durak, Alpay Medetalibeyoglu, Ulas Bagci,
• Abstract summary: Pulmonary Embolism (PE) is a leading cause of mortality and critical illness. Conventional clinical methods have limited success in predicting 30-day in-hospital mortality of PE patients.
• Score: 0.5942186563711294
• License:
• Abstract: Pulmonary Embolism (PE) is a serious cardiovascular condition that remains a leading cause of mortality and critical illness, underscoring the need for enhanced diagnostic strategies. Conventional clinical methods have limited success in predicting 30-day in-hospital mortality of PE patients. In this study, we present a new algorithm, called PEP-Net, for 30-day mortality prediction of PE patients based on the initial imaging data (CT) that opportunistically integrates a 3D Residual Network (3DResNet) with Extreme Gradient Boosting (XGBoost) algorithm with patient level binary labels without annotations of the emboli and its extent. Our proposed system offers a comprehensive prediction strategy by handling class imbalance problems, reducing overfitting via regularization, and reducing the prediction variance for more stable predictions. PEP-Net was tested in a cohort of 193 volumetric CT scans diagnosed with Acute PE, and it demonstrated a superior performance by significantly outperforming baseline models (76-78\%) with an accuracy of 94.5\% (+/-0.3) and 94.0\% (+/-0.7) when the input image is either lung region (Lung-ROI) or heart region (Cardiac-ROI). Our results advance PE prognostics by using only initial imaging data, setting a new benchmark in the field. While purely deep learning models have become the go-to for many medical classification (diagnostic) tasks, combined ResNet and XGBoost models herein outperform sole deep learning models due to a potential reason for having lack of enough data.

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