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Harmantepe AT, Dulger UC, Gonullu E, Dikicier E, Şentürk A, Eröz E. A method for predicting mortality in acute mesenteric ischemia: Machine learning. ULUS TRAVMA ACIL CER 2024; 30:487-492. [PMID: 38967529 PMCID: PMC11331353 DOI: 10.14744/tjtes.2024.48074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/02/2024] [Accepted: 06/07/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI). METHODS A total of 122 patients diagnosed with AMI at Sakarya University Training and Research Hospital between January 2011 and June 2023 were included in the study. These patients were divided into a training cohort (n=97) and a validation cohort (n=25), and further categorized as survivors and non-survivors during hospitalization. Serum-based laboratory results served as features. Hyperfeatures were eliminated using Recursive Feature Elimination (RFE) in Python to optimize outcomes. ML algorithms and data analyses were performed using Python (version 3.7). RESULTS Of the patients, 56.5% were male (n=69) and 43.5% were female (n=53). The mean age was 71.9 years (range 39-94 years). The mortality rate during hospitalization was 50% (n=61). To achieve optimal results, the model incorporated features such as age, red cell distribution width (RDW), C-reactive protein (CRP), D-dimer, lactate, globulin, and creatinine. Success rates in test data were as follows: logistic regression (LG), 80%; random forest (RF), 60%; k-nearest neighbor (KN), 52%; multilayer perceptron (MLP), 72%; and support vector classifier (SVC), 84%. A voting classifier (VC), aggregating votes from all models, achieved an 84% success rate. Among the models, SVC (sensitivity 1.0, specificity 0.77, area under the curve (AUC) 0.90, Confidence Interval (95%): (0.83-0.84)) and VC (sensitivity 1.0, specificity 0.77, AUC 0.88, Confidence Interval (95%): (0.83-0.84)) were noted for their effectiveness. CONCLUSION Independent risk factors for mortality were identified in patients with AMI. An efficient and rapid method using various ML models to predict mortality has been developed.
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Affiliation(s)
- Ahmet Tarık Harmantepe
- Department of Gastroenterology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Ugur Can Dulger
- Department of General Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Emre Gonullu
- Department of Gastroenterology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Enis Dikicier
- Department of General Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Adem Şentürk
- Department of Oncology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Erhan Eröz
- Department of General Surgery, Sakarya University Training and Research Hospital, Sakarya-Türkiye
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Geng Z, Yang C, Zhao Z, Yan Y, Guo T, Liu C, Wu A, Wu X, Wei L, Tian Y, Hu P, Wang K. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage. J Transl Med 2024; 22:236. [PMID: 38439097 PMCID: PMC10910789 DOI: 10.1186/s12967-024-04896-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/14/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH. METHODS Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model's performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform. RESULTS A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705-0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model's variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients. CONCLUSION We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.
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Affiliation(s)
- Zhi Geng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Chaoyi Yang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Ziye Zhao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Yibing Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Tao Guo
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Chaofan Liu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Aimei Wu
- Department of Neurology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Ling Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China.
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China.
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Zhang Y, Li G. Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms. Sci Rep 2024; 14:5157. [PMID: 38431726 PMCID: PMC10908806 DOI: 10.1038/s41598-024-55941-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024] Open
Abstract
Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.
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Affiliation(s)
- Yufeng Zhang
- Department of Vascular Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, Shandong, China
- Postdoctoral Workstation, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250021, Shandong, China
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, 214400, Jiangsu, China
| | - Gang Li
- Department of Vascular Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, Shandong, China.
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Zhang Y, Wang C, Xia Q, Jiang W, Zhang H, Amiri-Ardekani E, Hua H, Cheng Y. Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis. Front Med (Lausanne) 2023; 10:1001813. [PMID: 36860337 PMCID: PMC9968813 DOI: 10.3389/fmed.2023.1001813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
Objective This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms. Methods Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined. Results A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils. Conclusion COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.
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Affiliation(s)
- Yufeng Zhang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Cong Wang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Qingqing Xia
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Weilong Jiang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Huizhe Zhang
- Department of Respiratory Medicine, Yancheng Hospital of Traditional Chinese Medicine, Yancheng Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu, China
| | - Ehsan Amiri-Ardekani
- Department of Phytopharmaceuticals (Traditional Pharmacy), Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran,*Correspondence: Ehsan Amiri-Ardekani,
| | - Haibing Hua
- Department of Gastroenterology, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China,Haibing Hua,
| | - Yi Cheng
- Department of Respiratory Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Yi Cheng,
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Sun C, Li M, Lan L, Pei L, Zhang Y, Tan G, Zhang Z, Huang Y. Prediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches. Front Oncol 2023; 13:1096468. [PMID: 36923433 PMCID: PMC10009151 DOI: 10.3389/fonc.2023.1096468] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/06/2023] [Indexed: 03/03/2023] Open
Abstract
Purpose This study aimed to develop prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches and evaluate their performance. Methods The study was a secondary analysis based on a high-quality dataset from a randomized controlled trial (NCT00418457), including patients with primary breast cancer undergoing mastectomy. The primary outcome was CPSP at 12 months after surgery, defined as modified Brief Pain Inventory > 0. The dataset was randomly split into a training dataset (90%) and a testing dataset (10%). Variables were selected using recursive feature elimination combined with clinical experience, and potential predictors were then incorporated into three machine learning models, including random forest, gradient boosting decision tree and extreme gradient boosting models for outcome prediction, as well as logistic regression. The performances of these four models were tested and compared. Results 1152 patients were finally included, of which 22.1% developed CPSP at 12 months after breast cancer surgery. The 6 leading predictors were higher numerical rating scale within 2 days after surgery, post-menopausal status, urban medical insurance, history of at least one operation, under fentanyl with sevoflurane general anesthesia, and received axillary lymph node dissection. Compared with the multivariable logistic regression model, machine learning models showed better specificity, positive likelihood ratio and positive predictive value, helping to identify high-risk patients more accurately and create opportunities for early clinical intervention. Conclusions Our study developed prediction models for CPSP after breast cancer surgery based on machine learning approaches, which may help to identify high-risk patients and improve patients' management after breast cancer.
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Affiliation(s)
- Chen Sun
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Mohan Li
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ling Lan
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Lijian Pei
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Outcomes Research Consortium, Cleveland, OH, United States
| | - Yuelun Zhang
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Gang Tan
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhiyong Zhang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yuguang Huang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics. Int J Mol Sci 2022; 23:ijms232214155. [PMID: 36430631 PMCID: PMC9697273 DOI: 10.3390/ijms232214155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/31/2022] [Accepted: 11/12/2022] [Indexed: 11/18/2022] Open
Abstract
Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.
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Adams J, Agyenkwa-Mawuli K, Agyapong O, Wilson MD, Kwofie SK. EBOLApred: A machine learning-based web application for predicting cell entry inhibitors of the Ebola virus. Comput Biol Chem 2022; 101:107766. [DOI: 10.1016/j.compbiolchem.2022.107766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/10/2022] [Accepted: 08/29/2022] [Indexed: 11/03/2022]
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Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks. Int J Comput Assist Radiol Surg 2022; 17:1303-1311. [PMID: 35290645 DOI: 10.1007/s11548-022-02587-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/24/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Computed tomography (CT) images can display internal organs of patients and are particularly suitable for preoperative surgical diagnoses. The increasing demands for computer-aided systems in recent years have facilitated the development of many automated algorithms, especially deep convolutional neural networks, to segment organs and tumors or identify diseases from CT images. However, performances of some systems are highly affected by the amount of training data, while the sizes of medical image data sets, especially three-dimensional (3D) data sets, are usually small. This condition limits the application of deep learning. METHODS In this study, given a practical clinical data set that has 3D CT images of 20 patients with renal carcinoma, we designed a pipeline employing transfer learning to alleviate the detrimental effect of the small sample size. A dual-channel fine segmentation network (FS-Net) was constructed to segment kidney and tumor regions, with 210 publicly available 3D images from a competition employed during the training phase. We also built discriminative classifiers to classify the benign and malignant tumors based on the segmented regions, where both handcrafted and deep features were tested. RESULTS Our experimental results showed that the Dice values of segmented kidney and tumor regions were 0.9662 and 0.7685, respectively, which were better than those of state-of-the-art methods. The classification model using radiomics features can classify most of the tumors correctly. CONCLUSIONS The designed FS-Net was demonstrated to be more effective than simply fine-tuning on the practical small size data set given that the model can borrow knowledge from large auxiliary data without diluting the signal in primary data. For the small data set, radiomics features outperformed deep features in the classification of benign and malignant tumors. This work highlights the importance of architecture design in transfer learning, and the proposed pipeline is anticipated to provide a reference and inspiration for small data analysis.
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Abstract
Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.
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Khuzani AZ, Heidari M, Shariati SA. COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.05.09.20096560. [PMID: 32511510 PMCID: PMC7273278 DOI: 10.1101/2020.05.09.20096560] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections make the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we were able to successfully implement our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
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Affiliation(s)
- Abolfazl Zargari Khuzani
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, CA
| | - Morteza Heidari
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK
| | - S. Ali Shariati
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA
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Kanamori T, Segawa H, Yamamuro T, Kuwayama K, Tsujikawa K, Iwata YT. Metabolism of a new synthetic opioid tetrahydrofuranylfentanyl in fresh isolated human hepatocytes: Detection and confirmation of ring-opened metabolites. Drug Test Anal 2019; 12:439-448. [PMID: 31797567 DOI: 10.1002/dta.2743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 11/26/2019] [Accepted: 11/28/2019] [Indexed: 11/08/2022]
Abstract
The metabolism of a new synthetic opioid tetrahydrofuranylfentanyl (THF-fentanyl) was investigated using fresh human hepatocytes. Fourteen metabolites of THF-fentanyl, such as tetrahydrofuran ring-opened metabolites, desphenethylated metabolites, hydroxylated metabolites, and hydroxylated and methoxylated metabolites and their glucuronides, were detected in the culture medium of hepatocytes incubated with THF-fentanyl. Six metabolites, i.e. desphenethylated metabolite, 4'-hydroxy-THF-fentanyl, β-hydroxy-THF-fentanyl, 4'-hydroxy-3'-methoxy-THF-fentanyl, ring-opened alcohol metabolite, and ring-opened carboxylic acid metabolite, were identified via chemically synthesized authentic standards. A ring-opened alcohol metabolite and a ring-opened carboxylic acid metabolite are thought to be formed by reduction or oxidation of the intermediate aldehyde, which was formed by ring-opening of the metabolite hydroxylated at the carbon atom adjacent to the oxygen atom of the tetrahydrofuran ring. A ring-opened carboxylic acid metabolite was the main metabolite of THF-fentanyl based on the peak intensity.
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Affiliation(s)
- Tatsuyuki Kanamori
- National Research Institute of Police Science, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Hiroki Segawa
- National Research Institute of Police Science, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Tadashi Yamamuro
- National Research Institute of Police Science, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Kenji Kuwayama
- National Research Institute of Police Science, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Kenji Tsujikawa
- National Research Institute of Police Science, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Yuko T Iwata
- National Research Institute of Police Science, Kashiwanoha, Kashiwa, Chiba, Japan
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