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Draganich C, Anderson D, Dornan GJ, Sevigny M, Berliner J, Charlifue S, Welch A, Smith A. Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning. Spinal Cord 2024:10.1038/s41393-024-01008-2. [PMID: 38890506 DOI: 10.1038/s41393-024-01008-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/12/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024]
Abstract
STUDY DESIGN Retrospective multi-site cohort study. OBJECTIVES To develop an accurate machine learning predictive model using predictor variables from the acute rehabilitation period to determine ambulatory status in spinal cord injury (SCI) one year post injury. SETTING Model SCI System (SCIMS) database between January 2000 and May 2019. METHODS Retrospective cohort study using data that were previously collected as part of the SCI Model System (SCIMS) database. A total of 4523 patients were analyzed comparing traditional models (van Middendorp and Hicks) compared to machine learning algorithms including Elastic Net Penalized Logistic Regression (ENPLR), Gradient Boosted Machine (GBM), and Artificial Neural Networks (ANN). RESULTS Compared with GBM and ANN, ENPLR was determined to be the preferred model based on predictive accuracy metrics, calibration, and variable selection. The primary metric to judge discrimination was the area under the receiver operating characteristic curve (AUC). When compared to the van Middendorp all patients (0.916), ASIA A and D (0.951) and ASIA B and C (0.775) and Hicks all patients (0.89), ASIA A and D (0.934) and ASIA B and C (0.775), ENPLR demonstrated improved AUC for all patients (0.931), ASIA A and D (0.965) ASIA B and C (0.803). CONCLUSIONS Utilizing artificial intelligence and machine learning methods are feasible for accurately classifying outcomes in SCI and may provide improved sensitivity in identifying which individuals are less likely to ambulate and may benefit from augmentative strategies, such as neuromodulation. Future directions should include the use of additional variables to further refine these models.
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Affiliation(s)
- Christina Draganich
- University of Colorado Department of Physical Medicine and Rehabilitation, Aurora, CO, USA.
| | | | | | | | - Jeffrey Berliner
- University of Colorado Department of Physical Medicine and Rehabilitation, Aurora, CO, USA
- Craig Hospital, Englewood, CO, USA
| | | | | | - Andrew Smith
- University of Colorado Department of Physical Medicine and Rehabilitation, Aurora, CO, USA
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Yoo HJ, Koo B, Yong CW, Lee KS. Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury. Medicine (Baltimore) 2024; 103:e38286. [PMID: 38847729 PMCID: PMC11155515 DOI: 10.1097/md.0000000000038286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/26/2024] [Indexed: 06/10/2024] Open
Abstract
With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, there has been no attempt to predict walking ability after spinal cord injury (SCI) based on ML. In this situation, the main purpose of this study was to predict gait recovery after SCI at discharge from an acute rehabilitation facility using various ML algorithms. In addition, we explored important variables that were related to the prognosis. Finally, we attempted to suggest an ML-based decision support system (DSS) for predicting gait recovery after SCI. Data were collected retrospectively from patients with SCI admitted to an acute rehabilitation facility between June 2008 to December 2021. Linear regression analysis and ML algorithms (random forest [RF], decision tree [DT], and support vector machine) were used to predict the functional ambulation category at the time of discharge (FAC_DC) in patients with traumatic or non-traumatic SCI (n = 353). The independent variables were age, sex, duration of acute care and rehabilitation, comorbidities, neurological information entered into the International Standards for Neurological Classification of SCI worksheet, and somatosensory-evoked potentials at the time of admission to the acute rehabilitation facility. In addition, the importance of variables and DT-based DSS for FAC_DC was analyzed. As a result, RF and DT accurately predicted the FAC_DC measured by the root mean squared error. The root mean squared error of RF and the DT were 1.09 and 1.24 for all participants, 1.20 and 1.06 for those with trauma, and 1.12 and 1.03 for those with non-trauma, respectively. In the analysis of important variables, the initial FAC was found to be the most influential factor in all groups. In addition, we could provide a simple DSS based on strong predictors such as the initial FAC, American Spinal Injury Association Impairment Scale grades, and neurological level of injury. In conclusion, we provide that ML can accurately predict gait recovery after SCI for the first time. By focusing on important variables and DSS, we can guide early prognosis and establish personalized rehabilitation strategies in acute rehabilitation hospitals.
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Affiliation(s)
- Hyun-Joon Yoo
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul, Republic of Korea
| | - Bummo Koo
- School of Health and Environmental Science, Korea University College of Health Science, Seoul, Republic of Korea
| | - Chan-woo Yong
- School of Health and Environmental Science, Korea University College of Health Science, Seoul, Republic of Korea
| | - Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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Deng HW, Li BR, Zhou SD, Luo C, Lv BH, Dong ZM, Qin C, Hu RT. Revealing Novel Genes Related to Parkinson's Disease Pathogenesis and Establishing an associated Model. Neuroscience 2024; 544:64-74. [PMID: 38458535 DOI: 10.1016/j.neuroscience.2024.02.018] [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: 12/13/2023] [Revised: 02/15/2024] [Accepted: 02/20/2024] [Indexed: 03/10/2024]
Abstract
Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.
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Affiliation(s)
- Hao-Wei Deng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Bin-Ru Li
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, China
| | - Shao-Dan Zhou
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, China
| | - Chun Luo
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, China
| | - Bing-Hua Lv
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Zi-Mei Dong
- Department of Neurology, People's Hospital of Chuxiong, Yi Autonomous Prefecture, Chuxiong, Yunnan, China
| | - Chao Qin
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Rui-Ting Hu
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, China.
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Wang SY, Barrette LX, Ng JJ, Sangal NR, Cannady SB, Brody RM, Bur AM, Brant JA. Predicting reoperation and readmission for head and neck free flap patients using machine learning. Head Neck 2024. [PMID: 38357827 DOI: 10.1002/hed.27690] [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: 06/06/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery. METHODS Data were extracted from the 2012-2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30-day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated. RESULTS Four-hundred and sixty-six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine-hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58. CONCLUSION XGBoost was used to predict 30-day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision-making and improve data collection in future database iterations.
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Affiliation(s)
- Stephanie Y Wang
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Louis-Xavier Barrette
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jinggang J Ng
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Neel R Sangal
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Steven B Cannady
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert M Brody
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Corporal Michael J. Crescenz VAMC, Philadelphia, Pennsylvania, USA
| | - Andrés M Bur
- Department of Otolaryngology - Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jason A Brant
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Corporal Michael J. Crescenz VAMC, Philadelphia, Pennsylvania, USA
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Kim Y, Lim M, Kim SY, Kim TU, Lee SJ, Bok SK, Park S, Han Y, Jung HY, Hyun JK. Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients. J Clin Med 2024; 13:990. [PMID: 38398304 PMCID: PMC10889422 DOI: 10.3390/jcm13040990] [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: 12/19/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.
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Affiliation(s)
- Yuna Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Myungeun Lim
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Seo Young Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Tae Uk Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Seong Jae Lee
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Soo-Kyung Bok
- Department of Rehabilitation Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea;
| | - Soojun Park
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Youngwoong Han
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Ho-Youl Jung
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Jung Keun Hyun
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
- Department of Nanobiomedical Science and BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Republic of Korea
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan 31116, Republic of Korea
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Karabacak M, Jagtiani P, Margetis K. The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries. World Neurosurg 2024; 182:e67-e90. [PMID: 38030070 DOI: 10.1016/j.wneu.2023.11.043] [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: 09/01/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVES The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. METHODS The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. RESULTS A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. CONCLUSIONS ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed.
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Affiliation(s)
- Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
| | - Pemla Jagtiani
- School of Medicine, SUNY Downstate Health Sciences University, New York, New York, USA
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Liu Z, Li H, Li W, Zhang F, Ouyang W, Wang S, Zhi A, Pan X. Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning. Interdiscip Sci 2023; 15:653-662. [PMID: 37470945 DOI: 10.1007/s12539-023-00581-z] [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: 03/03/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/21/2023]
Abstract
PURPOSE Studies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities. METHODS The Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110 mL/m2 or RV ejection fraction which is lower than 40%) and 20 normal subjects who suffered from both cardiac MRI. The subjects were separated into training and validation sets in a ratio of 7:3 and were modeled by utilizing a nerve net of deep-learning and six machine-learning algorithms. Eight MRI specialists from multiple centers independently determined whether each subject in the validation group had RV abnormalities. Model performance was evaluated based on the AUC, accuracy, recall, sensitivity and specificity. Furthermore, a preliminary assessment of patient disease risk was performed based on clinical information using a nomogram. RESULTS The deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8. CONCLUSIONS A deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.
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Affiliation(s)
- Zeye Liu
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Hang Li
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Wenchao Li
- Pediatric Cardiac Surgery, Henan Provincial People's Hospital, Huazhong Fuwai Hospital, Zhengzhou University People's Hospital, Zhengzhou, 450000, China
| | - Fengwen Zhang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Wenbin Ouyang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Shouzheng Wang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, 100037, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Aihua Zhi
- Department of Medical Imaging, Fuwai Yunnan Cardiovascular Hospital, Kunming, 650000, China
| | - Xiangbin Pan
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, China.
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, 100037, China.
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, China.
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Karabacak M, Margetis K. Precision medicine for traumatic cervical spinal cord injuries: accessible and interpretable machine learning models to predict individualized in-hospital outcomes. Spine J 2023; 23:1750-1763. [PMID: 37619871 DOI: 10.1016/j.spinee.2023.08.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/28/2023] [Accepted: 08/13/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND CONTEXT A traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result in significant morbidity and mortality. The cervical spine is the most commonly affected area, accounting for about 60% of all traumatic SCI cases. PURPOSE This study aims to employ machine learning (ML) algorithms to predict various outcomes, such as in-hospital mortality, nonhome discharges, extended length of stay (LOS), extended length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with cervical SCI (cSCI). STUDY DESIGN Our study was a retrospective machine learning classification study aiming to predict the outcomes of interest, which were binary categorical variables, in patients diagnosed with cSCI. PATIENT SAMPLE The data for this study were obtained from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database, which was queried to identify patients who suffered from cSCI between 2019 and 2021. OUTCOME MEASURES The outcomes of interest of our study were in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications. The study evaluated the models' performance using both graphical and numerical methods. The receiver operating characteristic (ROC) and precision-recall curves (PRC) were used to assess model performance graphically. Numerical evaluation metrics included AUROC, balanced accuracy, weighted area under PRC (AUPRC), weighted precision, and weighted recall. METHODS The study employed data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database to identify patients with cSCI. Four ML algorithms, namely XGBoost, LightGBM, CatBoost, and Random Forest, were utilized to develop predictive models. The most effective models were then incorporated into a publicly available web application designed to forecast the outcomes of interest. RESULTS There were 71,661 patients included in the analysis for the outcome mortality, 67,331 for the outcome nonhome discharges, 76,782 for the outcome prolonged LOS, 26,615 for the outcome prolonged ICU-LOS, and 72,132 for the outcome major complications. The algorithms exhibited an AUROC value range of 0.78 to 0.839 for in-hospital mortality, 0.806 to 0.815 for nonhome discharges, 0.679 to 0.742 for prolonged LOS, 0.666 to 0.682 for prolonged ICU-LOS, and 0.637 to 0.704 for major complications. An open access web application was developed as part of the study, which can generate predictions for individual patients based on their characteristics. CONCLUSIONS Our study suggests that ML models can be valuable in assessing risk for patients with cervical cSCI and may have considerable potential for predicting outcomes during hospitalization. ML models demonstrated good predictive ability for in-hospital mortality and nonhome discharges, fair predictive ability for prolonged LOS, but poor predictive ability for prolonged ICU-LOS and major complications. Along with these promising results, the development of a user-friendly web application that facilitates the integration of these models into clinical practice is a significant contribution of this study. The product of this study may have significant implications in clinical settings to personalize care, anticipate outcomes, facilitate shared decision making and informed consent processes for cSCI patients.
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Affiliation(s)
- Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, 1468 Madison (Ave), New York, 10029 NY, USA
| | - Konstantinos Margetis
- Department of Neurosurgery, Mount Sinai Health System, 1468 Madison (Ave), New York, 10029 NY, USA
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Liu J, Cao B, Luo Y, Chen X, Han H, Li L, Zeng J. Risk factors of major bleeding detected by machine learning method in patients undergoing liver resection with controlled low central venous pressure technique. Postgrad Med J 2023; 99:1280-1286. [PMID: 37794600 DOI: 10.1093/postmj/qgad087] [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: 06/22/2023] [Revised: 08/18/2023] [Accepted: 09/01/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Controlled low central venous pressure (CLCVP) technique has been extensively validated in clinical practices to decrease intraoperative bleeding during liver resection process; however, no studies to date have attempted to propose a scoring method to better understand what risk factors might still be responsible for bleeding when CLCVP technique was implemented. METHODS We aimed to use machine learning to develop a model for detecting the risk factors of major bleeding in patients who underwent liver resection using CLCVP technique. We reviewed the medical records of 1077 patients who underwent liver surgery between January 2017 and June 2020. We evaluated the XGBoost model and logistic regression model using stratified K-fold cross-validation (K = 5), and the area under the receiver operating characteristic curve, the recall rate, precision rate, and accuracy score were calculated and compared. The SHapley Additive exPlanations was employed to identify the most influencing factors and their contribution to the prediction. RESULTS The XGBoost classifier with an accuracy of 0.80 and precision of 0.89 outperformed the logistic regression model with an accuracy of 0.76 and precision of 0.79. According to the SHapley Additive exPlanations summary plot, the top six variables ranked from most to least important included intraoperative hematocrit, surgery duration, intraoperative lactate, preoperative hemoglobin, preoperative aspartate transaminase, and Pringle maneuver duration. CONCLUSIONS Anesthesiologists should be aware of the potential impact of increased Pringle maneuver duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique. What is already known on this topic-Low central venous pressure technique has already been extensively validated in clinical practices, with no prediction model for major bleeding. What this study adds-The XGBoost classifier outperformed logistic regression model for the prediction of major bleeding during liver resection with low central venous pressure technique. How this study might affect research, practice, or policy-anesthesiologists should be aware of the potential impact of increased PM duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique.
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Affiliation(s)
- Jing Liu
- Department of Anesthesiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Bingbing Cao
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
| | - Yuelian Luo
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
| | - Xianqing Chen
- Department of Hepatobiliary and Pancreatic Surgery, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Hong Han
- Department of Anesthesiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Li Li
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
| | - Jianfeng Zeng
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
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Li B, Verma R, Beaton D, Tamim H, Hussain MA, Hoballah JJ, Lee DS, Wijeysundera DN, de Mestral C, Mamdani M, Al‐Omran M. Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning. J Am Heart Assoc 2023; 12:e030508. [PMID: 37804197 PMCID: PMC10757546 DOI: 10.1161/jaha.123.030508] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/28/2023] [Indexed: 10/09/2023]
Abstract
Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.
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Affiliation(s)
- Ben Li
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health TorontoUniversity of TorontoCanada
- Institute of Medical ScienceUniversity of TorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoCanada
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in IrelandUniversity of Medicine and Health SciencesDublinIreland
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health TorontoUniversity of TorontoCanada
| | - Hani Tamim
- Faculty of Medicine, Clinical Research InstituteAmerican University of Beirut Medical CenterBeirutLebanon
- College of MedicineAlfaisal UniversityRiyadhKingdom of Saudi Arabia
| | - Mohamad A. Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Jamal J. Hoballah
- Division of Vascular and Endovascular Surgery, Department of SurgeryAmerican University of Beirut Medical CenterBeirutLebanon
| | - Douglas S. Lee
- Division of Cardiology, Peter Munk Cardiac CentreUniversity Health NetworkTorontoCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
| | - Duminda N. Wijeysundera
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
- Department of AnesthesiaSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
| | - Charles de Mestral
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health TorontoUniversity of TorontoCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
| | - Muhammad Mamdani
- Institute of Medical ScienceUniversity of TorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoCanada
- Data Science & Advanced Analytics, Unity Health TorontoUniversity of TorontoCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Leslie Dan Faculty of PharmacyUniversity of TorontoCanada
| | - Mohammed Al‐Omran
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health TorontoUniversity of TorontoCanada
- Institute of Medical ScienceUniversity of TorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoCanada
- College of MedicineAlfaisal UniversityRiyadhKingdom of Saudi Arabia
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Department of SurgeryKing Faisal Specialist Hospital and Research CenterRiyadhKingdom of Saudi Arabia
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11
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Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M, Czogalik Ł, Dudek P, Magiera M, Lis A, Paszkiewicz I, Nawrat Z, Cebula M, Gruszczyńska K. What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine. Diagnostics (Basel) 2023; 13:2582. [PMID: 37568945 PMCID: PMC10417718 DOI: 10.3390/diagnostics13152582] [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: 06/24/2023] [Revised: 07/19/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.
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Affiliation(s)
- Jakub Kufel
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland;
| | - Katarzyna Bargieł-Łączek
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Szymon Kocot
- Bright Coders’ Factory, Technologiczna 2, 45-839 Opole, Poland
| | - Maciej Koźlik
- Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Wiktoria Bartnikowska
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Michał Janik
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Łukasz Czogalik
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Piotr Dudek
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Mikołaj Magiera
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Anna Lis
- Cardiology Students’ Scientific Association at the III Department of Cardiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Iga Paszkiewicz
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Zbigniew Nawrat
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland;
| | - Maciej Cebula
- Individual Specialist Medical Practice Maciej Cebula, 40-754 Katowice, Poland;
| | - Katarzyna Gruszczyńska
- Department of Radiodiagnostics, Invasive Radiology and Nuclear Medicine, Department of Radiology and Nuclear Medicine, School of Medicine in Katowice, Medical University of Silesia, Medyków 14, 40-752 Katowice, Poland;
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Trakulpanitkit A, Tunthanathip T. Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand. Acute Crit Care 2023; 38:362-370. [PMID: 37652865 PMCID: PMC10497900 DOI: 10.4266/acc.2023.00094] [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: 01/17/2023] [Revised: 04/23/2023] [Accepted: 06/20/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Hydrocephalus (HCP) is one of the most significant concerns in neurosurgical patients because it can cause increased intracranial pressure (ICP), resulting in mortality and morbidity. To date, machine learning (ML) has been helpful in predicting continuous outcomes. The primary objective of the present study was to identify the factors correlated with ICP, while the secondary objective was to compare the predictive performances among linear, non-linear, and ML regression models for ICP prediction. METHODS A total of 412 patients with various types of HCP who had undergone ventriculostomy was retrospectively included in the present study, and intraoperative ICP was recorded following ventricular catheter insertion. Several clinical factors and imaging parameters were analyzed for the relationship with ICP by linear correlation. The predictive performance of ICP was compared among linear, non-linear, and ML regression models. RESULTS Optic nerve sheath diameter (ONSD) had a moderately positive correlation with ICP (r=0.530, P<0.001), while several ventricular indexes were not statistically significant in correlation with ICP. For prediction of ICP, random forest (RF) and extreme gradient boosting (XGBoost) algorithms had low mean absolute error and root mean square error values and high R2 values compared to linear and non-linear regression when the predictive model included ONSD and ventricular indexes. CONCLUSIONS The XGBoost and RF algorithms are advantageous for predicting preoperative ICP and establishing prognoses for HCP patients. Furthermore, ML-based prediction could be used as a non-invasive method.
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Affiliation(s)
- Avika Trakulpanitkit
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
| | - Thara Tunthanathip
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
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Jimenez C, Sparrey CJ, Narimani M. Identification of injured elements in computational models of spinal cord injury using machine learning . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082848 DOI: 10.1109/embc40787.2023.10340243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The purpose of this study was to use machine learning (ML) algorithms to identify tissue damage based on the mechanical outputs of computational models of spinal cord injury (SCI). Three datasets corresponding to gray matter, white matter, and the combination of gray and white matter tissues were used to train the models. These datasets were built from the comparison of histological images taken from SCI experiments in non-human primates and corresponding subject-specific finite element (FE) models. Four ML algorithms were evaluated and compared using cross-validation and the area under the receiver operating characteristic curve (AUC). After hyperparameter tuning, the AUC mean values for the algorithms ranged between 0.79 and 0.82, with a standard deviation no greater than 0.02. The findings of this study also showed that k-nearest neighbors and logistic regression algorithms were better at identifying injured elements than support vector machines and decision trees. Additionally, depending on the evaluated dataset, the mean values of other performance metrics, such as precision and recall, varied between algorithms. These initial results suggest that different algorithms might be more sensitive to the skewed distribution of classes in the studied datasets, and that identifying damage independently or simultaneously in the gray and white matter tissues might require a better definition of relevant features and the use of different ML algorithms. These approaches will contribute to improving the current understanding of the relationship between mechanical loading and tissue damage during SCI and will have implications for the development of prevention strategies for this condition.Clinical Relevance- Linking FE model predictions of mechanical loading to tissue damage is an essential step for FE models to provide clinically relevant information. Combined with imaging technologies, these models can provide useful insights to predict the extent of damage in animal subjects and guide the decision-making process during treatment planning.
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14
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Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients following urgent surgery within 24 h after injury. J Clin Neurosci 2023; 107:150-156. [PMID: 36376152 DOI: 10.1016/j.jocn.2022.11.003] [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: 08/14/2022] [Revised: 10/12/2022] [Accepted: 11/05/2022] [Indexed: 11/13/2022]
Abstract
We aimed to develop a machine learning (ML) model for predicting the neurological outcomes of cervical spinal cord injury (CSCI). We retrospectively analyzed 135 patients with CSCI who underwent surgery within 24 h after injury. Patients were assessed with the American Spinal Injury Association Impairment Scale (AIS; grades A to E) 6 months after injury. A total of 34 features extracted from demographic variables, surgical factors, laboratory variables, neurological status, and radiological findings were analyzed. The ML model was created using Light GBM, XGBoost, and CatBoost. We evaluated Shapley Additive Explanations (SHAP) values to determine the variables that contributed most to the prediction models. We constructed multiclass prediction models for the five AIS grades and binary classification models to predict more than one-grade improvement in AIS 6 months after injury. Of the ML models used, CatBoost showed the highest accuracy (0.800) for the prediction of AIS grade and the highest AUC (0.90) for predicting improvement in AIS. AIS grade at admission, intramedullary hemorrhage, longitudinal extent of intramedullary T2 hyperintensity, and HbA1c were identified as important features for these prediction models. The ML models successfully predicted neurological outcomes 6 months after injury following urgent surgery in patients with CSCI.
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15
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Spinal Cord Injury AIS Predictions Using Machine Learning. eNeuro 2023; 10:ENEURO.0149-22.2022. [PMID: 36543536 PMCID: PMC9831144 DOI: 10.1523/eneuro.0149-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 10/21/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
The study used machine learning to predict The American Spinal Injury Association Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital discharge time from hospital admission data. Additionally, machine learning was used to analyze the best model for feature importance to validate the criticality of the AIS score and highlight relevant demographic details. The data used for training machine learning models was from the National Spinal Cord Injury Statistical Center (NSCISC) database of U.S. spinal cord injury patient details. Eighteen real features were used from 417 provided features, which mapped to 53 machine learning features after processing. Eight models were tuned on the dataset to predict AIS scores, and Shapely analysis was performed to extract the most important of the 53 features. Patients within the NSCISC database who sustained injuries were between 1972 and 2016 after data cleaning (n = 20,790). Outcomes were test set multiclass accuracy and aggregated Shapely score magnitudes. Ridge Classifier was the best performer with 73.6% test set accuracy. AIS scores and neurologic category at the time of admission were the best predictors of recovery. Demographically, features were less important, but age, sex, marital status, and race stood out. AIS scores on admission are highly predictive of patient outcomes when combined with patient demographic data. Promising results in terms of predicting recovery were seen, and Shapely analysis allowed for the machine learning model to be probed as a whole, giving insight into overall feature trends.
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16
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Zhao F, Zhang H, Cheng D, Wang W, Li Y, Wang Y, Lu D, Dong C, Ren D, Yang L. Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models. Front Med (Lausanne) 2022; 9:1037944. [PMID: 36507527 PMCID: PMC9732087 DOI: 10.3389/fmed.2022.1037944] [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: 09/06/2022] [Accepted: 11/11/2022] [Indexed: 11/27/2022] Open
Abstract
Background Nodular thyroid disease is by far the most common thyroid disease and is closely associated with the development of thyroid cancer. Coal miners with chronic coal dust exposure are at higher risk of developing nodular thyroid disease. There are few studies that use machine learning models to predict the occurrence of nodular thyroid disease in coal miners. The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) models. Methods This is a retrospective clinical study in which 1,708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and their clinical physical examination data, including general information, laboratory tests and imaging findings, were collected. A synthetic minority oversampling technique (SMOTE) was used for sample balancing, and the data set was randomly split into a training and Test dataset in a ratio of 8:2. Lasso regression and correlation heat map were used to screen the predictors of the models, and five ML models, including Extreme Gradient Augmentation (XGBoost), Logistic Classification (LR), Gaussian Parsimonious Bayesian Classification (GNB), Neural Network Classification (MLP), and Complementary Parsimonious Bayesian Classification (CNB) for their predictive efficacy, and the model with the highest AUC was selected as the optimal model for predicting the occurrence of nodular thyroid disease in coal miners. Result Lasso regression analysis showed Age, H-DLC, HCT, MCH, PLT, and GGT as predictor variables for the ML models; in addition, heat maps showed no significant correlation between the six variables. In the prediction of nodular thyroid disease, the AUC results of the five ML models, XGBoost (0.892), LR (0.577), GNB (0.603), MLP (0.601), and CNB (0.543), with the XGBoost model having the largest AUC, the model can be applied in clinical practice. Conclusion In this research, all five ML models were found to predict the risk of nodular thyroid disease in coal miners, with the XGBoost model having the best overall predictive performance. The model can assist clinicians in quickly and accurately predicting the occurrence of nodular thyroid disease in coal miners, and in adopting individualized clinical prevention and treatment strategies.
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Affiliation(s)
- Feng Zhao
- The First Hospital of Anhui University of Science & Technology (Huainan First People’s Hospital), Huainan, China
| | - Hongzhen Zhang
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Danqing Cheng
- Graduate School of Bengbu Medical College, Bengbu, China
| | - Wenping Wang
- Graduate School of Bengbu Medical College, Bengbu, China
| | - Yongtian Li
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Yisong Wang
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Dekun Lu
- The First Hospital of Anhui University of Science & Technology (Huainan First People’s Hospital), Huainan, China
| | - Chunhui Dong
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Dingfei Ren
- Occupational Control Hospital of Huai He Energy Group, Huainan, Anhui, China
| | - Lixin Yang
- The First Hospital of Anhui University of Science & Technology (Huainan First People’s Hospital), Huainan, China,*Correspondence: Lixin Yang,
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Shin J, Lee J, Ko T, Lee K, Choi Y, Kim HS. Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness. J Pers Med 2022; 12:1899. [PMID: 36422075 PMCID: PMC9698354 DOI: 10.3390/jpm12111899] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/04/2022] [Accepted: 11/08/2022] [Indexed: 01/25/2024] Open
Abstract
The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.
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Affiliation(s)
- Juyoung Shin
- Health Promotion Center, Seoul St. Mary’s Hospital, Seoul 06591, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Joonyub Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Taehoon Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Kanghyuck Lee
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Yera Choi
- NAVER CLOVA AI Lab, Seongnam 13561, Korea
| | - Hun-Sung Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
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Wang C, Ma H, Zhang B, Hua T, Wang H, Wang L, Han L, Li Q, Wu W, Sun Y, Yang H, Lu X. Inhibition of IL1R1 or CASP4 attenuates spinal cord injury through ameliorating NLRP3 inflammasome-induced pyroptosis. Front Immunol 2022; 13:963582. [PMID: 35990672 PMCID: PMC9389052 DOI: 10.3389/fimmu.2022.963582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
Spinal cord injury (SCI) is a devastating trauma characterized by serious neuroinflammation and permanent neurological dysfunction. However, the molecular mechanism of SCI remains unclear, and few effective medical therapies are available at present. In this study, multiple bioinformatics methods were used to screen out novel targets for SCI, and the mechanism of these candidates during the progression of neuroinflammation as well as the therapeutic effects were both verified in a rat model of traumatic SCI. As a result, CASP4, IGSF6 and IL1R1 were identified as the potential diagnostic and therapeutic targets for SCI by computational analysis, which were enriched in NF-κB and IL6-JAK-STATA3 signaling pathways. In the injured spinal cord, these three signatures were up-regulated and closely correlated with NLRP3 inflammasome formation and gasdermin D (GSDMD) -induced pyroptosis. Intrathecal injection of inhibitors of IL1R1 or CASP4 improved the functional recovery of SCI rats and decreased the expression of these targets and inflammasome component proteins, such as NLRP3 and GSDMD. This treatment also inhibited the pp65 activation into the nucleus and apoptosis progression. In conclusion, our findings of the three targets shed new light on the pathogenesis of SCI, and the use of immunosuppressive agents targeting these proteins exerted anti-inflammatory effects against spinal cord inflammation by inhibiting NF-kB and NLRP3 inflammasome activation, thus blocking GSDMD -induced pyroptosis and immune activation.
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Affiliation(s)
- Chenfeng Wang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Hongdao Ma
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Bangke Zhang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Tong Hua
- Department of Anesthesiology, Shanghai Changzheng Hospital, Shanghai, China
| | - Haibin Wang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Liang Wang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Lin Han
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Qisheng Li
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Weiqing Wu
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Yulin Sun
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Haisong Yang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
- *Correspondence: Xuhua Lu, ; Haisong Yang,
| | - Xuhua Lu
- Department of Orthopaedics, Shanghai Changzheng Hospital, Shanghai, China
- *Correspondence: Xuhua Lu, ; Haisong Yang,
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Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, Valliyodan B, Nguyen HT, Batley J, Bayer PE, Edwards D. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC PLANT BIOLOGY 2022; 22:180. [PMID: 35395721 PMCID: PMC8991976 DOI: 10.1186/s12870-022-03559-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/21/2022] [Indexed: 05/26/2023]
Abstract
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.
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Affiliation(s)
- Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia
| | - Jakob Petereit
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Babu Valliyodan
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
- Department of Agriculture and Environmental Sciences, Lincoln University, Jefferson City, MO, 65101, USA
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Philipp E Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia.
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20
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Chou A, Torres-Espin A, Kyritsis N, Huie JR, Khatry S, Funk J, Hay J, Lofgreen A, Shah R, McCann C, Pascual LU, Amorim E, Weinstein PR, Manley GT, Dhall SS, Pan JZ, Bresnahan JC, Beattie MS, Whetstone WD, Ferguson AR. Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. PLoS One 2022; 17:e0265254. [PMID: 35390006 PMCID: PMC8989303 DOI: 10.1371/journal.pone.0265254] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/25/2022] [Indexed: 11/18/2022] Open
Abstract
Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.
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Affiliation(s)
- Austin Chou
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - Abel Torres-Espin
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - Nikos Kyritsis
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - J. Russell Huie
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - Sarah Khatry
- DataRobot, Inc., Boston, Massachusetts, United States of America
| | - Jeremy Funk
- DataRobot, Inc., Boston, Massachusetts, United States of America
| | - Jennifer Hay
- DataRobot, Inc., Boston, Massachusetts, United States of America
| | - Andrew Lofgreen
- DataRobot, Inc., Boston, Massachusetts, United States of America
| | - Rajiv Shah
- DataRobot, Inc., Boston, Massachusetts, United States of America
| | - Chandler McCann
- DataRobot, Inc., Boston, Massachusetts, United States of America
| | - Lisa U. Pascual
- Orthopedic Trauma Institute, Department of Orthopedic Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
| | - Edilberto Amorim
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
- Department of Neurology, University of California, San Francisco (UCSF), San Francisco, California, United States of America
| | - Philip R. Weinstein
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurology, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Weill Institute for Neurosciences, Institute for Neurodegenerative Diseases, Spine Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
| | - Geoffrey T. Manley
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - Sanjay S. Dhall
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - Jonathan Z. Pan
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Anesthesia and Perioperative Care, University of California, San Francisco (UCSF), San Francisco, California, United States of America
| | - Jacqueline C. Bresnahan
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - Michael S. Beattie
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
| | - William D. Whetstone
- Department of Emergency Medicine, University of California, San Francisco (UCSF), San Francisco, California, United States of America
| | - Adam R. Ferguson
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Department of Neurological Surgery, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, United States of America
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21
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Lee S, Lee E, Park SS, Park MS, Jung J, Min GJ, Park S, Lee SE, Cho BS, Eom KS, Kim YJ, Lee S, Kim HJ, Min CK, Cho SG, Lee JW, Hwang HJ, Yoon JH. Prediction and recommendation by machine learning through repetitive internal validation for hepatic veno-occlusive disease/sinusoidal obstruction syndrome and early death after allogeneic hematopoietic cell transplantation. Bone Marrow Transplant 2022; 57:538-546. [DOI: 10.1038/s41409-022-01583-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 12/23/2022]
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22
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Determining the short-term neurological prognosis for acute cervical spinal cord injury using machine learning. J Clin Neurosci 2022; 96:74-79. [PMID: 34998207 DOI: 10.1016/j.jocn.2021.11.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/25/2021] [Accepted: 11/30/2021] [Indexed: 11/22/2022]
Abstract
It is challenging to predict neurological outcomes of acute spinal cord injury (SCI) considering issues such as spinal shock and injury heterogeneity. Deep learning-based radiomics (DLR) were developed to quantify the radiographic characteristics automatically using a convolutional neural network (CNN), and to potentially allow the prognostic stratification of patients. We aimed to determine the functional prognosis of patients with cervical SCI using machine learning approach based on MRI and to assess the ability to predict the neurological outcomes. We retrospectively analyzed the medical records of SCI patients (n=215) who had undergone MRI and had an American Spinal cord Injury Association Impairment Scale (AIS) assessment at 1 month after injury, enrolled with a total of 294 MR images. Sagittal T2-weighted MR images were used for the CNN training and validation. The deep learning framework TensorFlow was used to construct the CNN architecture. After we calculated the probability of the AIS grade using the DLR, we built the identification model based upon the random forest using 3 features: the probability of each AIS grade obtained by the DLR method, age, and the initial AIS grade at admission. We performed a statistical evaluation between the actual and predicted AIS. The accuracy, precision, recall and f1 score of the ensemble model based on the DLR and RF were 0.714, 0.590, 0.565 and 0.567, respectively. The present study demonstrates that prediction of the short-term neurological outcomes for acute cervical spinal cord injury based on MRI using machine learning is feasible.
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23
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Machine Learning Approach in Predicting Clinically Significant Improvements After Surgery in Patients with Cervical Ossification of the Posterior Longitudinal Ligament. Spine (Phila Pa 1976) 2021; 46:1683-1689. [PMID: 34027925 DOI: 10.1097/brs.0000000000004125] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN A retrospective analysis of prospectively collected data. OBJECTIVE This study aimed to create a prognostic model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using machine learning (ML). SUMMARY OF BACKGROUND DATA Determining surgical outcomes helps surgeons provide prognostic information to patients and manage their expectations. ML is a mathematical model that finds patterns from a large sample of data and makes predictions outperforming traditional statistical methods. METHODS Of 478 patients, 397 and 370 patients had complete follow-up information at 1 and 2 years, respectively, and were included in the analysis. A minimal clinically important difference (MCID) was defined as an acquired Japanese Orthopedic Association (JOA) score of ≥2.5 points, after which a ML model that predicts whether MCID can be achieved 1 and 2 years after surgery was created. Patient background, clinical symptoms, and imaging findings were used as variables for analysis. The ML model was created using LightGBM, XGBoost, random forest, and logistic regression, after which the accuracy and area under the receiver-operating characteristic curve (AUC) were calculated. RESULTS The mean JOA score was 10.3 preoperatively, 13.4 at 1 year after surgery, and 13.5 at 2 years after surgery. XGBoost showed the highest AUC (0.72) and high accuracy (67.8) for predicting MCID at 1 year, whereas random forest had the highest AUC (0.75) and accuracy (69.6) for predicting MCID at 2 years. Among the included features, total preoperative JOA score, duration of symptoms, body weight, sensory function of the lower extremity sub-score of the JOA, and age were identified as having the most significance in most of ML models. CONCLUSION Constructing a prognostic ML model for surgical outcomes in patients with OPLL is feasible, suggesting the potential application of ML for predictive models of spinal surgery.Level of Evidence: 4.
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24
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Ueno T, Ichikawa D, Shimizu Y, Narisawa T, Tsuji K, Ochi E, Sakurai N, Iwata H, Matsuoka YJ. Comorbid insomnia among breast cancer survivors and its prediction using machine learning: a nationwide study in Japan. Jpn J Clin Oncol 2021; 52:39-46. [PMID: 34718623 PMCID: PMC8721647 DOI: 10.1093/jjco/hyab169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/12/2021] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE Insomnia is an increasingly recognized major symptom of breast cancer which can seriously disrupt the quality of life during and many years after treatment. Sleep problems have also been linked with survival in women with breast cancer. The aims of this study were to estimate the prevalence of insomnia in breast cancers survivors, clarify the clinical characteristics of their sleep difficulties and use machine learning techniques to explore clinical insights. METHODS Our analysis of data, obtained in a nationwide questionnaire survey of breast cancer survivors in Japan, revealed a prevalence of suspected insomnia of 37.5%. With the clinical data obtained, we then used machine learning algorithms to develop a classifier that predicts comorbid insomnia. The performance of the prediction model was evaluated using 8-fold cross-validation. RESULTS When using optimal hyperparameters, the L2 penalized logistic regression model and the XGBoost model provided predictive accuracy of 71.5 and 70.6% for the presence of suspected insomnia, with areas under the curve of 0.76 and 0.75, respectively. Population segments with high risk of insomnia were also extracted using the RuleFit algorithm. We found that cancer-related fatigue is a predictor of insomnia in breast cancer survivors. CONCLUSIONS The high prevalence of sleep problems and its link with mortality warrants routine screening. Our novel predictive model using a machine learning approach offers clinically important insights for the early detection of comorbid insomnia and intervention in breast cancer survivors.
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Affiliation(s)
| | | | - Yoichi Shimizu
- Division of Health Care Research, Behavioral Science and Survivorship Research Group, Center for Public Health Sciences, National Cancer Center Japan, Tokyo, Japan.,Division of Nursing, National Cancer Center Hospital, Tokyo, Japan
| | - Tomomi Narisawa
- Division of Health Care Research, Behavioral Science and Survivorship Research Group, Center for Public Health Sciences, National Cancer Center Japan, Tokyo, Japan
| | - Katsunori Tsuji
- Division of Health Care Research, Behavioral Science and Survivorship Research Group, Center for Public Health Sciences, National Cancer Center Japan, Tokyo, Japan
| | - Eisuke Ochi
- Faculty of Bioscience and Applied Chemistry, Hosei University, Koganei, Tokyo, Japan
| | | | - Hiroji Iwata
- Department of Breast Oncology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yutaka J Matsuoka
- Division of Health Care Research, Behavioral Science and Survivorship Research Group, Center for Public Health Sciences, National Cancer Center Japan, Tokyo, Japan
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