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Aghababa MP, Andrysek J. Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis. PLoS One 2024; 19:e0300447. [PMID: 38564508 PMCID: PMC10987001 DOI: 10.1371/journal.pone.0300447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.
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Affiliation(s)
- Mohammad Pourmahmood Aghababa
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Jan Andrysek
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
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田 楚, 陈 翔, 朱 桓, 秦 晟, 石 柳, 芮 云. [Application and prospect of machine learning in orthopaedic trauma]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2023; 37:1562-1568. [PMID: 38130202 PMCID: PMC10739668 DOI: 10.7507/1002-1892.202308064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
Objective To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. Methods A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. Results The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. Conclusion The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
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Affiliation(s)
- 楚伟 田
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 翔溆 陈
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 桓毅 朱
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 晟博 秦
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 柳 石
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 云峰 芮
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
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Mo Y, Adu-Amankwaah J, Qin W, Gao T, Hou X, Fan M, Liao X, Jia L, Zhao J, Yuan J, Tan R. Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis. Ann Med 2023; 55:2279748. [PMID: 37983519 DOI: 10.1080/07853890.2023.2279748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/31/2023] [Indexed: 11/22/2023] Open
Abstract
The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.
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Affiliation(s)
- Yixuan Mo
- Department of Physiology, Basic medical school, Xuzhou Medical University, Xuzhou, China
| | - Joseph Adu-Amankwaah
- Department of Physiology, Basic medical school, Xuzhou Medical University, Xuzhou, China
| | - Wenjie Qin
- Department of Physiology, Basic medical school, Xuzhou Medical University, Xuzhou, China
- The Collaborative Innovation Center, Jining Medical University, Jining, Shandong, China
| | - Tan Gao
- The Collaborative Innovation Center, Jining Medical University, Jining, Shandong, China
| | - Xiaoqing Hou
- The Collaborative Innovation Center, Jining Medical University, Jining, Shandong, China
| | - Mengying Fan
- The Collaborative Innovation Center, Jining Medical University, Jining, Shandong, China
| | - Xuemei Liao
- The Collaborative Innovation Center, Jining Medical University, Jining, Shandong, China
| | - Liwei Jia
- Department of Pathology, UT Southwestern Medical Center, Dallas, UT, USA
| | - Jinming Zhao
- Department of Pathology, College of Basic Medical Sciences, China Medical University, Shenyang, China
- Department of Pathology, The First Hospital of China Medical University, Shenyang, China
| | - Jinxiang Yuan
- The Collaborative Innovation Center, Jining Medical University, Jining, Shandong, China
| | - Rubin Tan
- Department of Physiology, Basic medical school, Xuzhou Medical University, Xuzhou, China
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Kong X, Zhang K. A novel text sentiment analysis system using improved depthwise separable convolution neural networks. PeerJ Comput Sci 2023; 9:e1236. [PMID: 37346624 PMCID: PMC10280403 DOI: 10.7717/peerj-cs.1236] [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: 11/23/2022] [Accepted: 01/11/2023] [Indexed: 06/23/2023]
Abstract
Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people's emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks.
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Affiliation(s)
- Xiaoyu Kong
- Wuxi Vocational Institute of Commerce, Wuxi, Jiangsu, China
| | - Ke Zhang
- Wuxi Vocational Institute of Commerce, Wuxi, Jiangsu, China
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Takkavatakarn K, Hofer IS. Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:53-60. [PMID: 36723283 DOI: 10.1053/j.akdh.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/30/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022]
Abstract
Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Nephrology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Ira S Hofer
- Department of Anesthesiology, Pain and Perioperative Medicine, Icahn School of Medicine at Mount, Sinai, NY.
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Zhang N, Pan LY, Chen WY, Ji HH, Peng GQ, Tang ZW, Wang HL, Jia YT, Gong J. A Risk-Factor Model for Antineoplastic Drug-Induced Serious Adverse Events in Cancer Inpatients: A Retrospective Study Based on the Global Trigger Tool and Machine Learning. Front Pharmacol 2022; 13:896104. [PMID: 35847000 PMCID: PMC9277092 DOI: 10.3389/fphar.2022.896104] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
The objective of this study was to apply a machine learning method to evaluate the risk factors associated with serious adverse events (SAEs) and predict the occurrence of SAEs in cancer inpatients using antineoplastic drugs. A retrospective review of the medical records of 499 patients diagnosed with cancer admitted between January 1 and December 31, 2017, was performed. First, the Global Trigger Tool (GTT) was used to actively monitor adverse drug events (ADEs) and SAEs caused by antineoplastic drugs and take the number of positive triggers as an intermediate variable. Subsequently, risk factors with statistical significance were selected by univariate analysis and least absolute shrinkage and selection operator (LASSO) analysis. Finally, using the risk factors after the LASSO analysis as covariates, a nomogram based on a logistic model, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting (AdaBoost), light-gradient-boosting machine (LightGBM), random forest (RF), gradient-boosting decision tree (GBDT), decision tree (DT), and ensemble model based on seven algorithms were used to establish the prediction models. A series of indicators such as the area under the ROC curve (AUROC) and the area under the PR curve (AUPR) was used to evaluate the model performance. A total of 94 SAE patients were identified in our samples. Risk factors of SAEs were the number of triggers, length of stay, age, number of combined drugs, ADEs occurred in previous chemotherapy, and sex. In the test cohort, a nomogram based on the logistic model owns the AUROC of 0.799 and owns the AUPR of 0.527. The GBDT has the best predicting abilities (AUROC = 0.832 and AUPR = 0.557) among the eight machine learning models and was better than the nomogram and was chosen to establish the prediction webpage. This study provides a novel method to accurately predict SAE occurrence in cancer inpatients.
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Affiliation(s)
- Ni Zhang
- National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Department of Pharmacy, Children’s Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- School of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Ling-Yun Pan
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
| | - Wan-Yi Chen
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
| | - Huan-Huan Ji
- National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Department of Pharmacy, Children’s Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Gui-Qin Peng
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
| | - Zong-Wei Tang
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
| | - Hui-Lai Wang
- Department of Information Center, The University Town Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yun-Tao Jia, ; Hui-Lai Wang, ; Jun Gong,
| | - Yun-Tao Jia
- National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Department of Pharmacy, Children’s Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- School of Pharmacy, Chongqing Medical University, Chongqing, China
- *Correspondence: Yun-Tao Jia, ; Hui-Lai Wang, ; Jun Gong,
| | - Jun Gong
- Department of Information Center, The University Town Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yun-Tao Jia, ; Hui-Lai Wang, ; Jun Gong,
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