Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

Abstract

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.

Use of artificial intelligence and radiomics for risk stratification in patients with pulmonary embolism: New tools for an old problem

“…Su et al. obtained some novel results analysing clinical and laboratory variables of PE patients reporting an accuracy for their AI‐derived prediction model in evaluating risk classes of 99.26% and a sensitivity of 98.57% 12 . Similarly, Gao et al.…”

“…Su et al obtained some novel results analysing clinical and laboratory variables of PE patients reporting an accuracy for their AIderived prediction model in evaluating risk classes of 99.26% and a sensitivity of 98.57%. 12 Similarly, Gao et al created an AI tool that effectively predicts deterioration in patients with non-high-risk acute PE, based on the right ventricle/left ventricle (RV/LV) 4-chamber diameter ratio and the presence of pulmonary vein filling abnormalities. In their validation cohort, the nomogram achieved a sensitivity of 88.5% and a specificity of 83.4%.…”

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

“…Overall, 15 studies explored ML-based models to assist VTE diagnosis, either in the form of a pretest probability or to assist diagnosis after clinical presentation (Supplementary Table 4) [79][80][81][82][83][84][85][86][87][88][89][90][91][92][93] . Most of them do not describe clearly preprocessing steps, splitting/cross-validation, hyperparameters, and other performance metrics.…”

Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction

“…To verify the generalization ability of the proposed method, bGEBA-SVM was compared with other methods by six public datasets [58,59] for comparative experiments, and Table 12 shows the details of these datasets.…”