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Zhu J, Shan Y, Li Y, Xu X, Wu X, Xue Y, Gao G. Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury. Intensive Care Med Exp 2024; 12:58. [PMID: 38954280 PMCID: PMC11219663 DOI: 10.1186/s40635-024-00643-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/17/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients. METHODS We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting. RESULTS The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results. CONCLUSIONS The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.
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Affiliation(s)
- Jun Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yihua Li
- Department of Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Xuxu Xu
- Department of Neurosurgery, Minhang Hospital Fudan University, Shanghai, 201199, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yajun Xue
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China.
| | - Guoyi Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
- Neurotrauma Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, China.
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Zhu J, Shan Y, Li Y, Wu X, Gao G. Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms. World Neurosurg 2024; 185:e1348-e1360. [PMID: 38519020 DOI: 10.1016/j.wneu.2024.03.085] [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: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/24/2024]
Abstract
OBJECTIVE This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI). METHODS A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score. RESULTS The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance. CONCLUSIONS This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.
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Affiliation(s)
- Jun Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yihua Li
- Department of Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoyi Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Zhu G, Ozkara BB, Chen H, Zhou B, Jiang B, Ding VY, Wintermark M. Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study. Neuroradiol J 2024; 37:74-83. [PMID: 37921691 PMCID: PMC10863571 DOI: 10.1177/19714009231212364] [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] [Indexed: 11/04/2023] Open
Abstract
PURPOSE We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. METHODS In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. RESULTS Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. CONCLUSION Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.
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Affiliation(s)
- Guangming Zhu
- Department of Neurology, The University of Arizona, USA
| | - Burak B Ozkara
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| | - Hui Chen
- Department of Neuroradiology, MD Anderson Cancer Center, USA
| | - Bo Zhou
- Neuroradiology Division, Department of Radiology, Stanford University, USA
| | - Bin Jiang
- Neuroradiology Division, Department of Radiology, Stanford University, USA
| | - Victoria Y Ding
- Quantitative Sciences Unit, Department of Medicine, Stanford University, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, USA
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Schonfeld E, Mordekai N, Berg A, Johnstone T, Shah A, Shah V, Haider G, Marianayagam NJ, Veeravagu A. Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations. Cureus 2024; 16:e51963. [PMID: 38333513 PMCID: PMC10851045 DOI: 10.7759/cureus.51963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024] Open
Abstract
Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.
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Affiliation(s)
- Ethan Schonfeld
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Alex Berg
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Thomas Johnstone
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Aaryan Shah
- School of Humanities and Sciences, Stanford University, Stanford, USA
| | - Vaibhavi Shah
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Ghani Haider
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Anand Veeravagu
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
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Li X, Dai A, Tran R, Wang J. Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective. Breast Cancer Res Treat 2023:10.1007/s10549-023-06996-y. [PMID: 37329459 DOI: 10.1007/s10549-023-06996-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 05/25/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND microRNA (miRNAs) are small, non-coding RNAs that mediate post-transcriptional gene silencing. Numerous studies have demonstrated the critical role of miRNAs in the development of breast cancer and ovarian cancer. To reduce potential bias from individual studies, a more comprehensive approach of exploring miRNAs in cancer research is essential. This study aims to explore the role of miRNAs in the development of breast cancer and ovarian cancer. METHODS Abstracts of the publications were tokenized and the biomedical terms (miRNA, gene, disease, species) were identified and extracted for vectorization. Predictive analyses were conducted with four machine learning models: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes. Both holdout validation and cross-validation were utilized. Feature importance will be identified for miRNA-cancer networks construction. RESULTS We found that miR-182 is highly specific to female cancers. miR-182 targets different genes in regulating breast cancer and ovarian cancer. Naïve Bayes provided a promising prediction model for breast cancer and ovarian cancer with miRNAs and genes combination, with an accuracy score greater than 60%. Feature importance identified miR-155 and miR-199 are critical for breast cancer and ovarian cancer prediction, with miR-155 being highly related to breast cancer, whereas miR-199 being more associated with ovarian cancer. CONCLUSION Our approach effectively identified potential miRNA biomarkers associated with breast cancer and ovarian cancer, providing a solid foundation for generating novel research hypotheses and guiding future experimental studies.
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Affiliation(s)
- Xin Li
- Ophthalmology Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, Shandong, China
| | - Andrea Dai
- Oakland University William Beaumont School of Medicine, Rochester, MI, 48309, USA
| | - Richard Tran
- Masters Program in Computer Science, University of Chicago, Chicago, IL, 20833, USA
| | - Jie Wang
- Applied Data Science Program, Syracuse University, Syracuse, NY, 13244, USA.
- MDSight, LLC, Brookeville, MD, 20833, USA.
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Tsuyuki C, Hiraga H, Sudo M, Ueda T, Seo K, Minatozaki M, Fukuda Y, Okuda Y, Iwasaki H, Naito H, Lu D. Estimability study on the age of toddlers' gait development based on gait parameters. Sci Rep 2023; 13:2958. [PMID: 36807628 PMCID: PMC9941512 DOI: 10.1038/s41598-023-30039-7] [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: 11/11/2022] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
In the first few years of toddlers' locomotion, various gait parameters improve gradually and dynamically with gait development. Therefore, in this study, we hypothesized that the age of gait development, or the level of gait development with age as its indicator, can be estimated from several gait parameters related to gait development, and investigated its estimability. In total, 97 healthy toddlers aged about 1-3 years participated in the study. All five selected gait parameters showed a moderate or higher correlation with age, but the duration with a large change and the strength of the association with gait development varied for each gait parameter. Multiple regression analysis was performed using age as the objective variable and five selected gait parameters as explanatory variables, and an estimation model (R2 = 0.683, adjusted R2 = 0.665) was created. The estimation model was verified using a test dataset separate from the training dataset (R2 = 0.82, p < 0.001). It was suggested that the age of gait development could be estimated from gait alone. Gait analysis based on empirical observations may reduce the need for skilled observers and their potential variability.
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Affiliation(s)
- Chisa Tsuyuki
- Tokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501, Japan. .,Graduate School of Health and Sports Science, Juntendo University, 1-1 Hirakagakuendai, Inzai, Chiba, 270-1695, Japan.
| | - Haruna Hiraga
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Motoki Sudo
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Tomoya Ueda
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Kanako Seo
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan ,grid.258269.20000 0004 1762 2738Graduate School of Health and Sports Science, Juntendo University, 1-1 Hirakagakuendai, Inzai, Chiba 270-1695 Japan
| | - Masayuki Minatozaki
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Yuko Fukuda
- grid.419719.30000 0001 0816 944XTochigi Research Laboratories, Kao Corporation, 2606 Akabane, Ichikai-machi, Haga-gun, Tochigi, 321-3497 Japan
| | - Yasuyuki Okuda
- grid.419719.30000 0001 0816 944XTochigi Research Laboratories, Kao Corporation, 2606 Akabane, Ichikai-machi, Haga-gun, Tochigi, 321-3497 Japan
| | - Hiroyuki Iwasaki
- Jujo Pediatric Clinic, 3-22-8-101 Kamijujo, Kita-ku, Tokyo, 114-0034 Japan
| | - Hisashi Naito
- grid.258269.20000 0004 1762 2738Graduate School of Health and Sports Science, Juntendo University, 1-1 Hirakagakuendai, Inzai, Chiba 270-1695 Japan
| | - Dajiang Lu
- grid.412543.50000 0001 0033 4148Department of Human Sports Science, Shanghai University of Sport, Shanghai, 200438 China
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Zou H, Yang W, Wang M, Zhu Q, Liang H, Wu H, Tang L. Predicting length of stay ranges by using novel deep neural networks. Heliyon 2023; 9:e13573. [PMID: 36852025 PMCID: PMC9958433 DOI: 10.1016/j.heliyon.2023.e13573] [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: 06/28/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Background and aims Accurately predicting length of stay (LOS) is considered a challenging task for health care systems globally. In previous studies on LOS range prediction, researchers commonly pre-classified the LOS ranges, which were the same for all patients in the same classification, and then utilized a classifier for prediction. In this study, we innovatively aimed to predict the specific LOS range for each patient (the LOS range was different for each patient). Methods In the modified deep neural network (DNN), the overall sample error (root mean square error (RMSE) method), the estimated sample error (ERRpred method), the probability distribution with different loss functions (Dispred_Loss1, Dispred_Loss2, and Dispred_Loss3 method), and the generative adversarial networks (WGAN-GP for LOS method) are used for LOS range prediction. The Medical Information Mart for Intensive Care III (MIMIC-III) database is used to validate these methods. Results The RMSE method is convenient for LOS range prediction, but the predicted ranges are all consistent in the same batch of samples. The ERRpred method can achieve better prediction results in samples with low errors. However, the prediction effect is worse in samples with larger errors. The Dispred_Loss1 method encounters a training instability problem. The Dispred_Loss2 and Dispred_Loss3 methods perform well in making predictions. Although WGAN-GP for LOS method does not show a substantial advantage over other methods, this method might have the potential to improve the predictive performance. Conclusion The results show that it is possible to achieve an acceptable accurate LOS range prediction through a reasonable model design, which may help physicians in the clinic.
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Affiliation(s)
- Hong Zou
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.,Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China
| | - Wei Yang
- Department of Urology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Meng Wang
- Department of Traditional Chinese Medicine, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Qiao Zhu
- Department of Obstetrics and Gynecology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Hongyin Liang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Hong Wu
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China
| | - Lijun Tang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
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Pan Y, Fang C, Zhu X, Wan J. Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study. Digit Health 2023; 9:20552076231217814. [PMID: 38053736 PMCID: PMC10695088 DOI: 10.1177/20552076231217814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/10/2023] [Indexed: 12/07/2023] Open
Abstract
Objective To investigate the mean impact value (MIV) method for discerning the most efficacious input variables for the machine learning (ML) model. Subsequently, various ML algorithms are harnessed to formulate a more accurate predictive model that can forecast both the prognosis and the length of hospital stay for patients suffering from traumatic brain injury (TBI). Design Retrospective cohort study. Participants The study retrospectively accrued data from 1128 cases of patients who sought medical intervention at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University, within the timeframe spanning from May 2017 to May 2022. Methods We performed a retrospective analysis of patient data obtained from the Neurosurgery Center of the Second Hospital of Anhui Medical University, covering the period from May 2017 to May 2022. Following meticulous data filtration and partitioning, 70% of the data were allocated for model training, while the remaining 30% served for model evaluation. During the construction phase of the ML models, a gamut of 11 independent variables-including, but not limited to, in-hospital complications and patient age-were utilized as input variables. Conversely, the length of stay (LOS) and the Glasgow Outcome Scale (GOS) scores were designated as output variables. The model architecture was initially refined through the MIV methodology to identify optimal input variables, whereupon five distinct predictive models were constructed, encompassing support vector regression (SVR), convolutional neural networks (CNN), backpropagation (BP) neural networks, artificial neural networks (ANN) and logistic regression (LR). Ultimately, SVR emerged as the most proficient predictive model and was further authenticated through an external dataset obtained from the First Hospital of Anhui Medical University. Results Upon incorporating the optimal input variables as ascertained through MIV, it was observed that the SVR model exhibited remarkable predictive prowess. Specifically, the Mean Absolute Percentage Error (MAPE) of the SVR model in predicting the GOS score in the test dataset is only 6.30%, and the MAPE in the external validation set is only 7.61%. In terms of predicting hospitalization time, the accuracy of the test and external validation sets were 9.28% and 7.91%, respectively. This error indicator is significantly lower than the error of other prediction models, thus proving the excellent efficacy and clinical reliability of the MIV-optimized SVR model. Conclusion This study unequivocally substantiates that the incorporation of MIV for selecting optimal input variables can substantially augment the predictive accuracy of machine learning models. Among the models examined, the MIV-SVR model emerged as the most accurate and clinically applicable, thereby rendering it highly conducive for future clinical decision-making processes.
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Affiliation(s)
- Yifeng Pan
- The School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, China
| | - Cheng Fang
- Department of Neurosurgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueling Zhu
- The School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, China
| | - Jinghai Wan
- Department of Neurosurgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Kuo H, Liu TW, Huang YP, Chin SC, Ro LS, Kuo HC. Differential Diagnostic Value of Machine Learning-Based Models for Embolic Stroke. Clin Appl Thromb Hemost 2023; 29:10760296231203663. [PMID: 37728185 PMCID: PMC10515586 DOI: 10.1177/10760296231203663] [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: 05/29/2023] [Revised: 08/10/2023] [Accepted: 09/11/2023] [Indexed: 09/21/2023] Open
Abstract
Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.).The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke.
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Affiliation(s)
- HsunYu Kuo
- Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Tsai-Wei Liu
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Yo-Ping Huang
- Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan
- Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu, Taiwan
- Fellow of the Institute of Electrical and Electronics Engineers, Taipei, Taiwan
- Fellow of the Institution of Engineering and Technology, Taipei, Taiwan
- Fellow of Chinese Automatic Control Society, Taipei, Taiwan
| | - Shy-Chyi Chin
- Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Long-Sun Ro
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Hung-Chou Kuo
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
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