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Cattaneo A, Ghidotti A, Catellani F, Fiorentino G, Vitali A, Regazzoni D, Rizzi C, Bombardieri E. Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis. Arch Orthop Trauma Surg 2024; 144:2347-2356. [PMID: 38483620 PMCID: PMC11093841 DOI: 10.1007/s00402-024-05245-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/17/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION Clinical gait analysis can be used to evaluate the recovery process of patients undergoing total hip arthroplasty (THA). The postoperative walking patterns of these patients can be significantly influenced by the choice of surgical approach, as each procedure alters distinct anatomical structures. The aim of this study is twofold. The first objective is to develop a gait model to describe the change in ambulation one week after THA. The secondary goal is to describe the differences associated with the surgical approach. MATERIALS AND METHODS Thirty-six patients undergoing THA with lateral (n = 9), anterior (n = 15), and posterior (n = 12) approaches were included in the study. Walking before and 7 days after surgery was recorded using a markerless motion capture system. Exploratory Factor Analysis (EFA), a data reduction technique, condensed 21 spatiotemporal gait parameters to a smaller set of dominant variables. The EFA-derived gait domains were utilized to study post-surgical gait variations and to compare the post-surgical gait among the three groups. RESULTS Four distinct gait domains were identified. The most pronounced variation one week after surgery is in the Rhythm (gait cycle time: + 32.9 % ), followed by Postural control (step width: + 27.0 % ), Phases (stance time: + 11.0 % ), and Pace (stride length: - 9.3 % ). In postsurgical walking, Phases is statistically significantly different in patients operated with the posterior approach compared to lateral (p-value = 0.017) and anterior (p-value = 0.002) approaches. Furthermore, stance time in the posterior approach group is significantly lower than in healthy individuals (p-value < 0.001). CONCLUSIONS This study identified a four-component gait model specific to THA patients. The results showed that patients after THA have longer stride time but shorter stride length, wider base of support, and longer stance time, although the posterior group had a statistically significant shorter stance time than the others. The findings of this research have the potential to simplify the reporting of gait outcomes, reduce redundancy, and inform targeted interventions in regards to specific gait domains.
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Affiliation(s)
- Andrea Cattaneo
- Department of Information Management Engineering and Production Engineering, University of Bergamo, Via Galvani, 2, Dalmine, BG, Italy.
| | - Anna Ghidotti
- Department of Information Management Engineering and Production Engineering, University of Bergamo, Via Galvani, 2, Dalmine, BG, Italy
| | | | | | - Andrea Vitali
- Department of Information Management Engineering and Production Engineering, University of Bergamo, Via Galvani, 2, Dalmine, BG, Italy
| | - Daniele Regazzoni
- Department of Information Management Engineering and Production Engineering, University of Bergamo, Via Galvani, 2, Dalmine, BG, Italy
| | - Caterina Rizzi
- Department of Information Management Engineering and Production Engineering, University of Bergamo, Via Galvani, 2, Dalmine, BG, Italy
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González-Castro A, Leirós-Rodríguez R, Prada-García C, Benítez-Andrades JA. The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review. J Med Internet Res 2024; 26:e54934. [PMID: 38684088 DOI: 10.2196/54934] [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: 11/28/2023] [Revised: 01/30/2024] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. OBJECTIVE The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. METHODS A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. RESULTS We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. CONCLUSIONS The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. TRIAL REGISTRATION PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.
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Affiliation(s)
- Ana González-Castro
- Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain
| | - Raquel Leirós-Rodríguez
- SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain
| | - Camino Prada-García
- Department of Preventive Medicine and Public Health, Universidad de Valladolid, Valladolid, Spain
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Yamamoto A, Fujita K, Yamada E, Ibara T, Nihey F, Inai T, Tsukamoto K, Kobayashi Y, Nakahara K, Okawa A. Gait characteristics in patients with distal radius fracture using an in-shoe inertial measurement system at various gait speeds. Gait Posture 2024; 107:317-323. [PMID: 37914562 DOI: 10.1016/j.gaitpost.2023.10.023] [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: 12/15/2022] [Revised: 09/07/2023] [Accepted: 10/26/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Distal radius fractures (DRF) commonly occur in early postmenopausal females as the first fragility fracture. Although the incidence of DRF in this set of patients may be related to a lower ability to control their balance and gait, the detailed gait characteristics of DRF patients have not been examined. RESEARCH QUESTION Is it possible to identify the physical and gait features of DRF patients using in-shoe inertial measurement unit (IMU) sensors at various gait speeds and to develop a machine learning (ML) algorithm to estimate patients with DRF using gait? METHODS In this cross-sectional case control study, we recruited 28 postmenopausal females with DRF as their first fragility fracture and 32 age-matched females without a history of fragility fractures. The participants underwent several physical and gait tests. In the gait performance test, the participants walked 16 m with the in-shoe IMU sensor at slower, preferred, and faster speeds. The gait parameters were calculated by the IMU, and we applied the ML technique using the extreme gradient boosting (XGBoost) algorithm to predict the presence of DRF. RESULTS The fracture group showed lower hand grip strength and lower ability to change gait speed. The difference in gait parameters was mainly observed at faster speeds. The amplitude of the change in the parameters was small in the fracture group. The XGBoost model demonstrated reasonable accuracy in predicting DRFs (area under the curve: 0.740), and the most relevant variable was the stance time at a faster speed. SIGNIFICANCE Gait analysis using in-shoe IMU sensors at different speeds is useful for evaluating the characteristics of DRFs. The obtained gait parameters allow the prediction of fractures using the XGBoost algorithm.
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Affiliation(s)
- Akiko Yamamoto
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Koji Fujita
- Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8519, Japan.
| | - Eriku Yamada
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Takuya Ibara
- Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Fumiyuki Nihey
- Environmental and Material Research Laboratories, NEC Corporation 1131, Hinode, Abiko-city, Chiba 270-1198, Japan
| | - Takuma Inai
- QOL and Materials Research Group, Health and Medical Research Institute, Department of Life Science and Technology, National Institute of Advanced Industrial Science and Technology, 2217-14 Hayashi-cho, Takamatsu-city, Kagawa 761-0301, Japan
| | - Kazuya Tsukamoto
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, 2-8-5 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Kentaro Nakahara
- Environmental and Material Research Laboratories, NEC Corporation 1131, Hinode, Abiko-city, Chiba 270-1198, Japan
| | - Atsushi Okawa
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
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Gregg E, Beggs C, Bissas A, Nicholson G. A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women. PLoS One 2023; 18:e0293729. [PMID: 37906588 PMCID: PMC10617741 DOI: 10.1371/journal.pone.0293729] [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: 06/08/2023] [Accepted: 10/17/2023] [Indexed: 11/02/2023] Open
Abstract
Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this cross-sectional study aimed to identify those functional variables (i.e. balance, gait and clinical measures) and physical characteristics (i.e. strength and body composition) that could best distinguish between older female fallers and non-fallers, using a machine learning approach. Overall, 60 community-dwelling older women (≥65 years), retrospectively classified as fallers (n = 21) or non-fallers (n = 39), attended three data collection sessions. Data (281 variables) collected from tests in five separate domains (balance, gait, clinical measures, strength and body composition) were analysed using random forest (RF) and leave-one-variable-out partial least squares correlation analysis (LOVO PLSCA) to assess variable importance. The strongest discriminators from each domain were then aggregated into a multi-domain dataset, and RF, LOVO PLSCA, and logistic regression models were constructed to identify the important variables in distinguishing between fallers and non-fallers. These models were used to classify participants as either fallers or non-fallers, with their performance evaluated using receiver operating characteristic (ROC) analysis. The study found that it is possible to classify fallers and non-fallers with a high degree of accuracy (e.g. logistic regression: sensitivity = 90%; specificity = 87%; AUC = 0.92; leave-one-out cross-validation accuracy = 63%) using a combination of 18 variables from four domains, with the gait and strength domains being particularly informative for screening programmes aimed at assessing falls risk.
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Affiliation(s)
- Emily Gregg
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
- York Health Economics Consortium, University of York, York, United Kingdom
| | - Clive Beggs
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
- Department of Medicine for the Elderly, Cambridge University Hospitals, Cambridge, United Kingdom
| | - Athanassios Bissas
- School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom
| | - Gareth Nicholson
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
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Tian CW, Chen XX, Shi L, Zhu HY, Dai GC, Chen H, Rui YF. Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients. World J Orthop 2023; 14:741-754. [PMID: 37970626 PMCID: PMC10642403 DOI: 10.5312/wjo.v14.i10.741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/08/2023] [Accepted: 09/28/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors. AIM To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. METHODS A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS. RESULTS The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex. CONCLUSION ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.
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Affiliation(s)
- Chu-Wei Tian
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Xiang-Xu Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Liu Shi
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Huan-Yi Zhu
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Guang-Chun Dai
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Hui Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Yun-Feng Rui
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
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Balan JR, Rodrigo H, Saxena U, Mishra SK. Explainable machine learning reveals the relationship between hearing thresholds and speech-in-noise recognition in listeners with normal audiograms. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2023; 154:2278-2288. [PMID: 37823779 DOI: 10.1121/10.0021303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 09/17/2023] [Indexed: 10/13/2023]
Abstract
Some individuals complain of listening-in-noise difficulty despite having a normal audiogram. In this study, machine learning is applied to examine the extent to which hearing thresholds can predict speech-in-noise recognition among normal-hearing individuals. The specific goals were to (1) compare the performance of one standard (GAM, generalized additive model) and four machine learning models (ANN, artificial neural network; DNN, deep neural network; RF, random forest; XGBoost; eXtreme gradient boosting), and (2) examine the relative contribution of individual audiometric frequencies and demographic variables in predicting speech-in-noise recognition. Archival data included thresholds (0.25-16 kHz) and speech recognition thresholds (SRTs) from listeners with clinically normal audiograms (n = 764 participants or 1528 ears; age, 4-38 years old). Among the machine learning models, XGBoost performed significantly better than other methods (mean absolute error; MAE = 1.62 dB). ANN and RF yielded similar performances (MAE = 1.68 and 1.67 dB, respectively), whereas, surprisingly, DNN showed relatively poorer performance (MAE = 1.94 dB). The MAE for GAM was 1.61 dB. SHapley Additive exPlanations revealed that age, thresholds at 16 kHz, 12.5 kHz, etc., on the order of importance, contributed to SRT. These results suggest the importance of hearing in the extended high frequencies for predicting speech-in-noise recognition in listeners with normal audiograms.
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Affiliation(s)
- Jithin Raj Balan
- Department of Speech, Language and Hearing Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Hansapani Rodrigo
- School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, Texas 78539, USA
| | - Udit Saxena
- Department of Audiology and Speech-Language Pathology, Gujarat Medical Education and Research Society, Medical College and Hospital, Ahmedabad, 380060, India
| | - Srikanta K Mishra
- Department of Speech, Language and Hearing Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
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Ouyang S, Zhang X, Li H, Tang X, Ning X, Li R, Ke P, Li Y, Huang F, Liu B, Fang Y, Liang Y. Cataract, glaucoma, and diabetic retinopathy are independent risk factors affecting falls in the older adult with eye diseases. Geriatr Nurs 2023; 53:170-174. [PMID: 37540912 DOI: 10.1016/j.gerinurse.2023.07.001] [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: 02/04/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 08/06/2023]
Abstract
OBJECTIVES Falls are the leading cause of injury-related hospitalization in older adult, presenting a significant public health concern. To examine the specific eye diseases for risk factors of falls in the older adult. METHODS A total of 775 older adults admitted to tertiary care hospitals were divided into a fall or non-fall group based on a questionnaire. Logistic regression analysis was used to identify factors associated with falls. RESULTS With 208 falls, 775 participants were recruited. The major associated factors of falls were older age (Odds ratios [OR]: 1.05), female (OR: 1.91), cardiovascular diseases (OR: 1.65), more outdoor activities (OR: 2.81), cataract (OR: 1.65), glaucoma (OR: 1.63), diabetic retinopathy (OR: 2.72). CONCLUSIONS Our study demonstrates that cataract, glaucoma, and diabetic retinopathy in the older adult with eye diseases are independent risk factors of falls, which may shed light on the prevention of falls in the older adult with eye diseases.
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Affiliation(s)
- Shuyi Ouyang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaoni Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Haojun Li
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xuan Tang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xueyan Ning
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Ruiwen Li
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Pingfang Ke
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yanan Li
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Fengxian Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Baoyi Liu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Ying Fang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Yingying Liang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Department of Ophthalmology, Guangzhou First people's Hospital, Guangzhou, China.
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Yang H, Luo YM, Ma CY, Zhang TY, Zhou T, Ren XL, He XL, Deng KJ, Yan D, Tang H, Lin H. A gender specific risk assessment of coronary heart disease based on physical examination data. NPJ Digit Med 2023; 6:136. [PMID: 37524859 PMCID: PMC10390496 DOI: 10.1038/s41746-023-00887-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .
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Affiliation(s)
- Hui Yang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ya-Mei Luo
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China
- Medical Engineering & Medical Informatics Integration and Transformational Medicine Key Laboratory of Luzhou City, Luzhou, 646000, China
| | - Cai-Yi Ma
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tian-Yu Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Zhou
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiao-Lei Ren
- Sichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China
| | - Xiao-Lin He
- Sichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China
| | - Ke-Jun Deng
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dan Yan
- Beijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, 646000, China.
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Liuzzi P, Carpinella I, Anastasi D, Gervasoni E, Lencioni T, Bertoni R, Carrozza MC, Cattaneo D, Ferrarin M, Mannini A. Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors. Sci Rep 2023; 13:8640. [PMID: 37244933 PMCID: PMC10224964 DOI: 10.1038/s41598-023-35744-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.
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Affiliation(s)
- Piergiuseppe Liuzzi
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
- Scuola Superiore Sant'Anna, Istituto di BioRobotica, 56025, Pontedera, Italy
| | - Ilaria Carpinella
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy.
| | - Denise Anastasi
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Elisa Gervasoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Tiziana Lencioni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Rita Bertoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | | | - Davide Cattaneo
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
- Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università di Milano, 20122, Milan, Italy
| | - Maurizio Ferrarin
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Andrea Mannini
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
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10
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Kang Y, Kim MG, Lim KM. Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost. Toxicol Res 2023; 39:295-305. [PMID: 37008690 PMCID: PMC10050629 DOI: 10.1007/s43188-022-00168-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/15/2022] [Accepted: 12/23/2022] [Indexed: 01/24/2023] Open
Abstract
Skin irritation test is an essential part of the safety assessment of chemicals. Recently, computational models to predict the skin irritation draw attention as alternatives to animal testing. We developed prediction models on skin irritation/corrosion of liquid chemicals using machine learning algorithms, with 34 physicochemical descriptors calculated from the structure. The training and test dataset of 545 liquid chemicals with reliable in vivo skin hazard classifications based on UN Globally Harmonized System [category 1 (corrosive, Cat 1), 2 (irritant, Cat 2), 3 (mild irritant, Cat 3), and no category (nonirritant, NC)] were collected from public databases. After the curation of input data through removal and correlation analysis, every model was constructed to predict skin hazard classification for liquid chemicals with 22 physicochemical descriptors. Seven machine learning algorithms [Logistic regression, Naïve Bayes, k-nearest neighbor, Support vector machine, Random Forest, Extreme gradient boosting (XGB), and Neural net] were applied to ternary and binary classification of skin hazard. XGB model demonstrated the highest accuracy (0.73-0.81), sensitivity (0.71-0.92), and positive predictive value (0.65-0.81). The contribution of physicochemical descriptors to the classification was analyzed using Shapley Additive exPlanations plot to provide an insight into the skin irritation of chemicals. Supplementary Information The online version contains supplementary material available at 10.1007/s43188-022-00168-8.
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Affiliation(s)
- Yeonsoo Kang
- College of Pharmacy, Ewha Womans University, Seoul, 03760 Republic of Korea
| | - Myeong Gyu Kim
- College of Pharmacy, Ewha Womans University, Seoul, 03760 Republic of Korea
| | - Kyung-Min Lim
- College of Pharmacy, Ewha Womans University, Seoul, 03760 Republic of Korea
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11
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Balamuthusamy S, Dhanabalsamy N, Thankavel B, Bala MS, Pfaffle A. Utility of a ML analytics on real time risk stratification and re-intervention risk prediction on AV access outcomes and cost. J Vasc Access 2023:11297298231156632. [PMID: 36847187 DOI: 10.1177/11297298231156632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Vascular access is the lifeline for patients on hemodialysis. The average survival rates of dialysis dependent patients have been improving over the last 5 years and hence their dialysis access needs longevity for uninterrupted optimal dialysis. With the lack of genomic vascular access failure predictors, there is an unmet need for predicting an event and the appropriate approach to mitigate recurrence of the event that could have cost and outcome implications. METHODS We performed a single center experience that extracted relevant clinical (access flow, laboratory data and CKD details), access intervention (prior interventions, type & location of lesion, type of balloon used, use of stents etc.) and demographic (age, vintage on dialysis, sex, social determinants, other medical conditions) data in real time and feeds it into validated ML algorithms to predict risk of reintervention. (Plexus EMR LLC). RESULTS About 200 prevalent hemodialysis patients with a AV graft or AV fistula were included for this analysis. Need for re-intervention and use of stent/ flow reduction/new access creation were the outcomes analyzed. Plexus EMR is a licensed Azure based platform. R software was used to develop the ML algorithms. Regression factors were developed to assess and test the validity of individual attributes across all the data attributes. Each patient had a real time risk calculator available to the interventionalist on risk of reintervention/ year. Of the 200 patients, 148 had a AV fistula and the remaining 52 had a AV graft. Mean interventions in the year prior to analysis was 1.8 in patients with AV fistulas and 3.4 in AV grafts which decreased to 1.1 in AV fistulas and to 2.4 in AV grafts (p < 0.01) post tool deployment. There were 62 AV graft thrombectomies done in the observation year and 62% of those were repeat thrombectomies. Stent utilization increased to 37 (22 in AV grafts and 15 in AV fistulas) and 2 patients had AV access flow reduction surgery. The cumulative cost (predicted) preintervention was $712,609 and decreased to $512,172 post intervention. Stent utilization increased by 68% in the evaluation year and 89% of the stents used were PTFE coated stents. CONCLUSION Utilizing AI with ML based algorithms that includes clinical, demographic and patency maintenance variables could become new standards of care to optimally manage AV accesses and lower cost of care.
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Affiliation(s)
- Saravanan Balamuthusamy
- PPG Health PA and Tarrant Vascular, Fort Worth, TX, USA
- Texas Research Institute, Fort Worth, TX, USA
- HCA Healthcare, Fort Worth, TX, USA
- Plexus EMR LLC, Dallas, TX, USA
- OptMyCare Inc, Fort Worth, TX, USA
| | | | - Bharath Thankavel
- HCA Healthcare, Fort Worth, TX, USA
- OptMyCare Inc, Fort Worth, TX, USA
| | - Manu S Bala
- Texas Research Institute, Fort Worth, TX, USA
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12
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Bargiotas I, Wang D, Mantilla J, Quijoux F, Moreau A, Vidal C, Barrois R, Nicolai A, Audiffren J, Labourdette C, Bertin-Hugaul F, Oudre L, Buffat S, Yelnik A, Ricard D, Vayatis N, Vidal PP. Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall. J Neurol 2023; 270:618-631. [PMID: 35817988 PMCID: PMC9886639 DOI: 10.1007/s00415-022-11251-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023]
Abstract
Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.
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Affiliation(s)
- Ioannis Bargiotas
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France. .,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.
| | - Danping Wang
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Juan Mantilla
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Flavien Quijoux
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,ORPEA Group, Puteaux, France
| | - Albane Moreau
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Catherine Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Otorhinolaryngology (ENT), AP-HP, Hôpital Universitaire Pitié Salpêtrière, Paris, 75013, France
| | - Remi Barrois
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Alice Nicolai
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Julien Audiffren
- Department of Neuroscience, University of Fribourg, Fribourg, Switzerland
| | - Christophe Labourdette
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | | | - Laurent Oudre
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Stephane Buffat
- Laboratoire d'accidentologie de biomécanique et du comportement des conducteurs, GIE Psa Renault Groupes, Nanterre, France
| | - Alain Yelnik
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Physical and Rehabilitation Medicine (PRM), AP- HP, GH St Louis, Lariboisière, F. Widal, Paris, 75010, France
| | - Damien Ricard
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Neurology, AP-HP, Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, 92140, France.,École d'application du Val-de-Grâce, Service de Santé des Armée, Paris, France
| | - Nicolas Vayatis
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Pierre-Paul Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
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13
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The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms. Healthcare (Basel) 2022; 11:healthcare11010047. [PMID: 36611508 PMCID: PMC9818612 DOI: 10.3390/healthcare11010047] [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: 11/04/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Falling is an important public health issue, and predicting the fall risk can reduce the incidence of injury events in the elderly. However, most of the existing studies may have additional human and financial costs for community workers and doctors. Therefore, it is socially important to identify elderly people who are at high fall risk through a reasonable and cost-effective method. We evaluated the potential of multifractal, machine learning algorithms to identify the elderly at high fall risk. We developed a 42-point calibration model of the human body and recorded the three-dimensional coordinate datasets. The stability of the motion trajectory is calculated by the multifractal algorithm and used as an input dimension to compare the performance of the six classifiers. The results showed that the instability of the faller group was significantly greater than that of the no-faller group in the male and female cohorts (p < 0.005), and the Gradient Boosting Decision Tree classifier showed the best performance. The findings could help elderly people at high fall risk to identify individualized risk factors and facilitate tailored fall interventions.
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14
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Chung J, Akter S, Han S, Shin Y, Choi TG, Kang I, Kim SS. Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers. Int J Mol Sci 2022; 24:ijms24010129. [PMID: 36613569 PMCID: PMC9820758 DOI: 10.3390/ijms24010129] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.
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Affiliation(s)
- Jinwook Chung
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Salima Akter
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Sunhee Han
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yoonhwa Shin
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Tae Gyu Choi
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Insug Kang
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
- Correspondence: (I.K.); (S.S.K.); Tel.: +82-2-961-0524 (S.S.K.)
| | - Sung Soo Kim
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
- Correspondence: (I.K.); (S.S.K.); Tel.: +82-2-961-0524 (S.S.K.)
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15
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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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16
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Fujita K, Hiyama T, Wada K, Aihara T, Matsumura Y, Hamatsuka T, Yoshinaka Y, Kimura M, Kuzuya M. Machine learning-based muscle mass estimation using gait parameters in community-dwelling older adults: A cross-sectional study. Arch Gerontol Geriatr 2022; 103:104793. [PMID: 35987032 DOI: 10.1016/j.archger.2022.104793] [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: 06/02/2022] [Revised: 08/03/2022] [Accepted: 08/13/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Loss of skeletal muscle mass is associated with numerous factors such as metabolic diseases, lack of independence, and mortality in older adults. Therefore, developing simple, safe, and reliable tools for assessing skeletal muscle mass is needed. Some studies recently reported that the risks of the incidence of geriatric conditions could be estimated by analyzing older adults' gait; however, no studies have assessed the association between gait parameters and skeletal muscle loss in older adults. In this study, we applied machine learning approach to the gait parameters derived from three-dimensional skeletal models to distinguish older adults' low skeletal muscle mass. We also identified the most important gait parameters for detecting low muscle mass. METHODS Sixty-six community-dwelling older adults were recruited. Thirty-two gait parameters were created using a three-dimensional skeletal model involving 10-meter comfortable walking. After skeletal muscle mass measurement using a bioimpedance analyzer, low muscle mass was judged in accordance with the guideline of the Asia Working Group for Sarcopenia. The eXtreme gradient boosting (XGBoost) model was applied to discriminate between low and high skeletal muscle mass. RESULTS Eleven subjects had a low muscle mass. The c-statistics, sensitivity, specificity, precision of the final model were 0.7, 59.5%, 81.4%, and 70.5%, respectively. The top three dominant gait parameters were, in order of strongest effect, stride length, hip dynamic range of motion, and trunk rotation variability. CONCLUSION Machine learning-based gait analysis is a useful approach to determine the low skeletal muscle mass of community-dwelling older adults.
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Affiliation(s)
- Kosuke Fujita
- Department of Community Healthcare and Geriatrics, Graduate School of Medicine, Nagoya University, Nagoya, Japan; Department of Prevention and Care Science, Center for Development of Advanced Medicine for Dementia, National Center for Geriatrics and Gerontology, Obu, Japan.
| | - Takahiro Hiyama
- Technology Division, Panasonic Holdings Corporation, Kadoma, Japan
| | - Kengo Wada
- Electric Works Company, Panasonic Corporation, Kadoma, Japan
| | - Takahiro Aihara
- Electric Works Company, Panasonic Corporation, Kadoma, Japan
| | | | | | - Yasuko Yoshinaka
- Department of Bioenvironment, Kyoto University of Advanced Science, Kameoka, Japan
| | - Misaka Kimura
- Department of Bioenvironment, Kyoto University of Advanced Science, Kameoka, Japan; Doshisha Women's College of Liberal Arts, Graduate School of Nursing, Kyotanabe, Japan
| | - Masafumi Kuzuya
- Department of Community Healthcare and Geriatrics, Graduate School of Medicine, Nagoya University, Nagoya, Japan
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17
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Pérez-Trujillo M, Curcio CL, Duque-Méndez N, Delgado A, Cano L, Gomez F. Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study. Aging Clin Exp Res 2022; 34:2761-2768. [PMID: 36070079 DOI: 10.1007/s40520-022-02227-4] [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: 01/31/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis. AIM This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model. METHODS A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment. RESULTS According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance. CONCLUSION The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.
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Affiliation(s)
- Manuel Pérez-Trujillo
- Departamento de Informática y Computación, Facultad de Administración, Grupo GAIA, Universidad Nacional de Colombia, Manizales, Colombia
| | - Carmen-Lucía Curcio
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia.
| | - Néstor Duque-Méndez
- Departamento de Informática y Computación, Facultad de Administración, Grupo GAIA, Universidad Nacional de Colombia, Manizales, Colombia
| | - Alejandra Delgado
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia
| | - Laura Cano
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia
| | - Fernando Gomez
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia
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Kim B, Youm C, Park H, Lee M, Choi H. Association of Muscle Mass, Muscle Strength, and Muscle Function with Gait Ability Assessed Using Inertial Measurement Unit Sensors in Older Women. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19169901. [PMID: 36011529 PMCID: PMC9407844 DOI: 10.3390/ijerph19169901] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 05/31/2023]
Abstract
Aging-related muscle atrophy is associated with decreased muscle mass (MM), muscle strength (MS), and muscle function (MF) and may cause motor control, balance, and gait pattern impairments. This study determined associations of three speed-based gait variables with loss of MM, MS, and MF in older women. Overall, 432 older women aged ≥65 performed appendicular skeletal muscle, handgrip strength, and five times sit-to-stand test to evaluate MM, MS, and MF. A gait test was performed at three speeds by modifying the preferred walking speed (PWS; slower walking speed (SWS); faster-walking speed (FWS)) on a straight 19 m walkway. Stride length (SL) at PWS was significantly associated with MM. FWS and coefficient of variance (CV) of double support phase (DSP) and DSP at PWS showed significant associations with MS. CV of step time and stride time at SWS, FWS, and single support phase (SSP) at PWS showed significant associations with MF. SL at PWS, DSP at FWS, CV of DSP at PWS, stride time at SWS, and CV of SSP at PWS showed significant associations with composite MM, MS, and MF variables. Our study indicated that gait tasks under continuous and various speed conditions are useful for evaluating MM, MS, and MF.
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Affiliation(s)
- Bohyun Kim
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
| | - Changhong Youm
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
- Department of Health Care and Science, Dong-A University, Busan 49315, Korea
| | - Hwayoung Park
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
| | - Myeounggon Lee
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hyejin Choi
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
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19
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Chen L, Wang Y, Zhao F. Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence. Bioinformatics 2022; 38:3164-3172. [PMID: 35389435 PMCID: PMC9890318 DOI: 10.1093/bioinformatics/btac214] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 03/04/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Though genome-wide association studies have identified tens of thousands of variants associated with complex traits and most of them fall within the non-coding regions, they may not be the causal ones. The development of high-throughput functional assays leads to the discovery of experimental validated non-coding functional variants. However, these validated variants are rare due to technical difficulty and financial cost. The small sample size of validated variants makes it less reliable to develop a supervised machine learning model for achieving a whole genome-wide prediction of non-coding causal variants. RESULTS We will exploit a deep transfer learning model, which is based on convolutional neural network, to improve the prediction for functional non-coding variants (NCVs). To address the challenge of small sample size, the transfer learning model leverages both large-scale generic functional NCVs to improve the learning of low-level features and context-specific functional NCVs to learn high-level features toward the context-specific prediction task. By evaluating the deep transfer learning model on three MPRA datasets and 16 GWAS datasets, we demonstrate that the proposed model outperforms deep learning models without pretraining or retraining. In addition, the deep transfer learning model outperforms 18 existing computational methods in both MPRA and GWAS datasets. AVAILABILITY AND IMPLEMENTATION https://github.com/lichen-lab/TLVar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Li Chen
- To whom correspondence should be addressed.
| | | | - Fengdi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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20
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Lee M, Noh Y, Youm C, Kim S, Park H, Noh B, Kim B, Choi H, Yoon H. Estimating Health-Related Quality of Life Based on Demographic Characteristics, Questionnaires, Gait Ability, and Physical Fitness in Korean Elderly Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182211816. [PMID: 34831575 PMCID: PMC8624167 DOI: 10.3390/ijerph182211816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 10/31/2021] [Accepted: 11/10/2021] [Indexed: 01/14/2023]
Abstract
The elderly population in South Korea accounted for 15.5% of the total population in 2019. Thus, it is important to study the various elements governing the process of healthy aging. Therefore, this study investigated multiple prediction models to determine the health-related quality of life (HRQoL) in elderly adults based on the demographics, questionnaires, gait ability, and physical fitness. We performed eight physical fitness tests on 775 participants wearing shoe-type inertial measurement units and completing walking tasks at slower, preferred, and faster speeds. The HRQoL for physical and mental components was evaluated using a 36-item, short-form health survey. The prediction models based on multiple linear regression with feature importance were analyzed considering the best physical and mental components. We used 11 variables and 5 variables to form the best subset of features underlying the physical and mental components, respectively. We laid particular emphasis on evaluating the functional endurance, muscle strength, stress level, and falling risk. Furthermore, stress, insomnia severity, number of diseases, lower body strength, and fear of falling were taken into consideration in addition to mental-health-related variables. Thus, the study findings provide reliable and objective results to improve the understanding of HRQoL in elderly adults.
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Affiliation(s)
- Myeounggon Lee
- Center for Neuromotor and Biomechanics Research, Department of Health and Human Performance, University of Houston, Houston, TX 77004, USA;
| | - Yoonjae Noh
- Department of Management Information Systems, Dong-A University, Busan 49236, Korea; (Y.N.); (H.Y.)
| | - Changhong Youm
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
- Correspondence: (C.Y.); (S.K.); Tel.: +82-51-200-7830 (C.Y.); +82-05-200-7484 (S.K.); Fax: +82-51-200-7505 (C.Y.)
| | - Sangjin Kim
- Department of Management Information Systems, Dong-A University, Busan 49236, Korea; (Y.N.); (H.Y.)
- Correspondence: (C.Y.); (S.K.); Tel.: +82-51-200-7830 (C.Y.); +82-05-200-7484 (S.K.); Fax: +82-51-200-7505 (C.Y.)
| | - Hwayoung Park
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Byungjoo Noh
- Department of Kinesiology, Jeju National University, Jeju 63243, Korea;
| | - Bohyun Kim
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Hyejin Choi
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Hyemin Yoon
- Department of Management Information Systems, Dong-A University, Busan 49236, Korea; (Y.N.); (H.Y.)
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21
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Noh B, Yoon H, Youm C, Kim S, Lee M, Park H, Kim B, Choi H, Noh Y. Prediction of Decline in Global Cognitive Function Using Machine Learning with Feature Ranking of Gait and Physical Fitness Outcomes in Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111347. [PMID: 34769864 PMCID: PMC8582857 DOI: 10.3390/ijerph182111347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 11/30/2022]
Abstract
Gait and physical fitness are related to cognitive function. A decrease in motor function and physical fitness can serve as an indicator of declining global cognitive function in older adults. This study aims to use machine learning (ML) to identify important features of gait and physical fitness to predict a decline in global cognitive function in older adults. A total of three hundred and six participants aged seventy-five years or older were included in the study, and their gait performance at various speeds and physical fitness were evaluated. Eight ML models were applied to data ranked by the p-value (LP) of linear regression and the importance gain (XI) of XGboost. Five optimal features were selected using elastic net on the LP data for men, and twenty optimal features were selected using support vector machine on the XI data for women. Thus, the important features for predicting a potential decline in global cognitive function in older adults were successfully identified herein. The proposed ML approach could inspire future studies on the early detection and prevention of cognitive function decline in older adults.
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Affiliation(s)
- Byungjoo Noh
- Department of Kinesiology, Jeju National University, Jeju 63243, Korea;
| | - Hyemin Yoon
- Department of Management Information Systems, Dong-A University, Busan 49315, Korea; (H.Y.); (Y.N.)
| | - Changhong Youm
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
- Correspondence: (C.Y.); (S.K.); Tel.: +82-51-200-7830 (C.Y.); +82-05-200-7484 (S.K.); Fax: +82-51-200-7505 (C.Y.)
| | - Sangjin Kim
- Department of Management Information Systems, Dong-A University, Busan 49315, Korea; (H.Y.); (Y.N.)
- Correspondence: (C.Y.); (S.K.); Tel.: +82-51-200-7830 (C.Y.); +82-05-200-7484 (S.K.); Fax: +82-51-200-7505 (C.Y.)
| | - Myeounggon Lee
- Department of Health and Human Performance, University of Houston, Houston, TX 77004, USA;
| | - Hwayoung Park
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Bohyun Kim
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Hyejin Choi
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Yoonjae Noh
- Department of Management Information Systems, Dong-A University, Busan 49315, Korea; (H.Y.); (Y.N.)
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