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Bin C, Li Q, Tang J, Dai C, Jiang T, Xie X, Qiu M, Chen L, Yang S. Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease. Front Cardiovasc Med 2023; 10:1178782. [PMID: 37808888 PMCID: PMC10556651 DOI: 10.3389/fcvm.2023.1178782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
Introduction Cardiovascular disease (CVD) is a group of diseases involving the heart or blood vessels and represents a leading cause of death and disability worldwide. Carotid plaque is an important risk factor for CVD that can reflect the severity of atherosclerosis. Accordingly, developing a prediction model for carotid plaque formation is essential to assist in the early prevention and management of CVD. Methods In this study, eight machine learning algorithms were established, and their performance in predicting carotid plaque risk was compared. Physical examination data were collected from 4,659 patients and used for model training and validation. The eight predictive models based on machine learning algorithms were optimized using the above dataset and 10-fold cross-validation. The Shapley Additive Explanations (SHAP) tool was used to compute and visualize feature importance. Then, the performance of the models was evaluated according to the area under the receiver operating characteristic curve (AUC), feature importance, accuracy and specificity. Results The experimental results indicated that the XGBoost algorithm outperformed the other machine learning algorithms, with an AUC, accuracy and specificity of 0.808, 0.749 and 0.762, respectively. Moreover, age, smoke, alcohol drink and BMI were the top four predictors of carotid plaque formation. It is feasible to predict carotid plaque risk using machine learning algorithms. Conclusions This study indicates that our models can be applied to routine chronic disease management procedures to enable more preemptive, broad-based screening for carotid plaque and improve the prognosis of CVD patients.
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Affiliation(s)
- Chengling Bin
- Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China
| | - Qin Li
- Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China
| | - Jing Tang
- Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China
| | - Chaorong Dai
- Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China
| | - Ting Jiang
- Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China
| | - Xiufang Xie
- Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Neijiang, Neijiang, China
| | - Min Qiu
- Special Inspection Department, The First People’s Hospital of Neijiang, Neijiang, China
| | - Lumiao Chen
- Laboratory Department, The First People’s Hospital of Neijiang, Neijiang, China
| | - Shaorong Yang
- Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China
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Zhuo X, Lv J, Chen B, Liu J, Luo Y, Liu J, Xie X, Lu J, Zhao N. Combining conventional ultrasound and ultrasound elastography to predict HER2 status in patients with breast cancer. Front Physiol 2023; 14:1188502. [PMID: 37501928 PMCID: PMC10369848 DOI: 10.3389/fphys.2023.1188502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/30/2023] [Indexed: 07/29/2023] Open
Abstract
Introduction: Identifying the HER2 status of breast cancer patients is important for treatment options. Previous studies have shown that ultrasound features are closely related to the subtype of breast cancer. Methods: In this study, we used features of conventional ultrasound and ultrasound elastography to predict HER2 status. Results and Discussion: The performance of model (AUROC) with features of conventional ultrasound and ultrasound elastography is higher than that of the model with features of conventional ultrasound (0.82 vs. 0.53). The SHAP method was used to explore the interpretability of the models. Compared with HER2- tumors, HER2+ tumors usually have greater elastic modulus parameters and microcalcifications. Therefore, we concluded that the features of conventional ultrasound combined with ultrasound elastography could improve the accuracy for predicting HER2 status.
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Affiliation(s)
- Xiaoying Zhuo
- Ultrasound Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- Medical Imaging College of Xuzhou Medical University, Xuzhou, China
| | - Ji Lv
- Emergency Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Binjie Chen
- Emergency Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jia Liu
- Pathology Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yujie Luo
- Ultrasound Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jie Liu
- Ultrasound Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xiaowei Xie
- Ultrasound Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jiao Lu
- Ultrasound Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Ningjun Zhao
- Emergency Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- Laboratory of Emergency Medicine, Second Clinical Medical College of Xuzhou Medical University, Xuzhou, China
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Zhao Y, Jia L, Jia R, Han H, Feng C, Li X, Wei Z, Wang H, Zhang H, Pan S, Wang J, Guo X, Yu Z, Li X, Wang Z, Chen W, Li J, Li T. A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning. Shock 2022; 57:48-56. [PMID: 34905530 PMCID: PMC8663521 DOI: 10.1097/shk.0000000000001842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 12/29/2022]
Abstract
ABSTRACT Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.
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Affiliation(s)
- Yuzhuo Zhao
- Department of Emergency, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lijing Jia
- Department of Emergency, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ruiqi Jia
- School of Economics and Management, Beijing Jiaotong University, Beijing, China
| | - Hui Han
- Department of Emergency, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Cong Feng
- Department of Emergency, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xueyan Li
- Management School, Beijing Union University, Beijing, China
| | | | - Hongxin Wang
- Department of Emergency, Armed Police Characteristic Medical Center, Tianjin, China
| | - Heng Zhang
- Department of Emergency, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shuxiao Pan
- College of Computer Science and Artificial Intelligence, Wenzhou University
| | - Jiaming Wang
- School of Economics and Management, Beijing Jiaotong University, Beijing, China
| | - Xin Guo
- Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zheyuan Yu
- School of Economics and Management, Beijing Jiaotong University, Beijing, China
| | - Xiucheng Li
- School of Economics and Management, Beijing Jiaotong University, Beijing, China
| | - Zhaohong Wang
- School of Economics and Management, Beijing Jiaotong University, Beijing, China
| | - Wei Chen
- Department of Emergency, The Third Medical Center of Chinese PLA General Hospital, Beijing, China
- Hainan Hospital of Chinese PLA General Hospital, Sanya, China
| | - Jing Li
- School of Economics and Management, Beijing Jiaotong University, Beijing, China
| | - Tanshi Li
- Department of Emergency, The First Medical Center of Chinese PLA General Hospital, Beijing, China
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Ravindran SM, Bhaskaran SKM, Ambat SKN. A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter. Environ. Process. 2021; 8:1567-1599. [PMCID: PMC8486967 DOI: 10.1007/s40710-021-00543-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/22/2021] [Indexed: 06/02/2023]
Abstract
Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R2) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R2. The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.
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Affiliation(s)
- Sowmya Mangalath Ravindran
- Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala 682022 India
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