Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach

Abstract

In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management to improve patient prognosis. This study sought to develop and validate machine learning (ML) models leveraging clinical data and noncontrast CT radiomics to pinpoint patients at risk of postoperative rehemorrhage, equipping clinicians with an early detection tool for prompt intervention. The study conducted a retrospective analysis on 609 HICH patients, dividing them into training and external verification cohorts. These patients were categorized into groups with and without postoperative rehemorrhage. Radiomics features from noncontrast CT images were extracted, standardized, and employed to create several ML models. These models underwent internal validation using both radiomics and clinical data, with the best model’s feature significance assessed via the Shapley additive explanations (SHAP) method, then externally validated. In the study of 609 patients, postoperative rehemorrhage rates were similar in the training (18.8%, 80/426) and external verification (17.5%, 32/183) cohorts. Six significant noncontrast CT radiomics features were identified, with the support vector machine (SVM) model outperforming others in both internal and external validations. SHAP analysis highlighted five critical predictors of postoperative rehemorrhage risk, encompassing three radiomics features from noncontrast CT and two clinical data indicators. This study highlights the effectiveness of an SVM model combining radiomics features from noncontrast CT and clinical parameters in predicting postoperative rehemorrhage among HICH patients. This approach enables timely and effective interventions, thereby improving patient outcomes.

Convolutional Neural Network-Based ECG Signal Classification Model: A Study on Addressing Class Imbalance and Enhancing Model Interpretability

“…In the task of ECG signal classification, deep learning models, especially CNNs, have demonstrated their high efficiency [1][2][3]18]. However, the decision-making processes of these models often lack transparency, widely referred to as the "black box" problem [6,25,26]. For the end-users of the models, including physicians and clinical experts, understanding the internal workings and decision logic of the models is crucial [27].…”

“…In the domain of ECG signal classification, SHAP value analysis has proven to be a powerful tool for identifying and interpreting key features that affect model predictions [7]. Existing studies have shown that SHAP values play a significant role in recognizing crucial frequency features in ECG signals, predicting ST-segment elevations for cardiac events, and offering other diagnostic insights [5,6,[25][26][27]. For instance, Rashed-Al-Mahfu et al [30] successfully demonstrated the effectiveness of SHAP value analysis in identifying key frequency features that impact ECG signal classification.…”