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Tao X, Jiang M, Liu Y, Hu Q, Zhu B, Hu J, Guo W, Wu X, Xiong Y, Shi X, Zhang X, Han X, Li W, Tong R, Long E. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Sci Rep 2023; 13:16437. [PMID: 37777593 PMCID: PMC10543442 DOI: 10.1038/s41598-023-43240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.
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Affiliation(s)
- Xue Tao
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Min Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, Sichuan, China
| | - Yumeng Liu
- Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Qi Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan, China
| | - Baoqiang Zhu
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Jiaqiang Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Wenmei Guo
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Yu Xiong
- Institute of Materia Medica, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, 100050, China
| | - Xia Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xueli Zhang
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Xu Han
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Wenyuan Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Enwu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China.
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Chen JQ, Chen YY, Du X, Tao HJ, Pu ZJ, Shi XQ, Yue SJ, Zhou GS, Shang EX, Tang YP, Duan JA. Fuzzy identification of bioactive components for different efficacies of rhubarb by the back propagation neural network association analysis of UPLC-Q-TOF/MS E and integrated effects. Chin Med 2022; 17:50. [PMID: 35473719 PMCID: PMC9040240 DOI: 10.1186/s13020-022-00612-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/14/2022] [Indexed: 11/15/2022] Open
Abstract
Background Rhei Radix et Rhizoma (rhubarb), as one of the typical representatives of multi-effect traditional Chinese medicines (TCMs), has been utilized in the treatment of various diseases due to its multicomponent nature. However, there are few systematic investigations for the corresponding effect of individual components in rhubarb. Hence, we aimed to develop a novel strategy to fuzzily identify bioactive components for different efficacies of rhubarb by the back propagation (BP) neural network association analysis of ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry for every data (UPLC-Q-TOF/MSE) and integrated effects. Methods Through applying the fuzzy chemical identification, most components of rhubarb were classified into different chemical groups. Meanwhile the integration effect values of different efficacies can be determined by animal experiment evaluation and multi-attribute comprehensive indexes. Then the BP neural network was employed for association analysis of components and different efficacies by correlating the component contents determined from UPLC-Q-TOF/MSE profiling and the integration effect values. Finally, the effect contribution of one type of components may be totaled to demonstrate the universal and individual characters for different efficacies of rhubarb. Results It suggested that combined anthraquinones, flavanols and their polymers may be the universal character to the multi-functional properties of rhubarb. Other components contributed to the individuality of rhubarb efficacies, including stilbene glycosides, anthranones and their dimers, free anthraquinones, chromones, gallic acid and gallotannins, butyrylbenzenes and their glycosides. Conclusions Our findings demonstrated that the bioactive components for different efficacies of rhubarb were not exactly the same and can be systematically differentiated by the network-oriented strategy. These efforts will advance our knowledge and understanding of the bioactive components in rhubarb and provide scientific evidence to support the expansion of its use in clinical applications and the further development of some products based on this medicinal herb. Supplementary information The online version contains supplementary material available at 10.1186/s13020-022-00612-9.
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Affiliation(s)
- Jia-Qian Chen
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), Shaanxi Key Laboratory of Chinese Medicine Fundamentals and New Drugs Research, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, 712046, Xi'an, Shaanxi Province, China.,Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
| | - Yan-Yan Chen
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), Shaanxi Key Laboratory of Chinese Medicine Fundamentals and New Drugs Research, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, 712046, Xi'an, Shaanxi Province, China
| | - Xia Du
- Shaanxi Academy of Traditional Chinese Medicine, 710003, Xi'an, Shaanxi Province, China
| | - Hui-Juan Tao
- Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
| | - Zong-Jin Pu
- Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
| | - Xu-Qin Shi
- Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
| | - Shi-Jun Yue
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), Shaanxi Key Laboratory of Chinese Medicine Fundamentals and New Drugs Research, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, 712046, Xi'an, Shaanxi Province, China
| | - Gui-Sheng Zhou
- Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
| | - Er-Xin Shang
- Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
| | - Yu-Ping Tang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), Shaanxi Key Laboratory of Chinese Medicine Fundamentals and New Drugs Research, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, 712046, Xi'an, Shaanxi Province, China.
| | - Jin-Ao Duan
- Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 210023, Nanjing, Jiangsu Province, China
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Su B. Using Metabolic and Biochemical Indicators to Predict Diabetic Retinopathy by Back-Propagation Artificial Neural Network. Diabetes Metab Syndr Obes 2021; 14:4031-4041. [PMID: 34552342 PMCID: PMC8450288 DOI: 10.2147/dmso.s322224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/24/2021] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Timely diagnosis of diabetic retinopathy (DR) can significantly improve the prognosis of patients. In this study, we established a prediction model by analyzing the relationship between diabetic retinopathy and related metabolic and biochemical indicators. METHODS A total of 427 type 2 diabetes mellitus (T2DM) patients were selected from the datadryad website data. Logistic regression (MLR) was used to input layer variables of the model were screened. Then, Tan-Sigmoid was selected as the transfer function of the hidden layer node, and the linear function was used as the output layer function to establish the back propagation artificial neural network (BP-ANN) model. The model was applied to 183 patients with type 2 diabetes mellitus (T2DM) in our hospital to predict DR. RESULTS A total of 167 patients (39.2%) with DR were obtained from the Datadryad database. Input variables were screened by MLR model, and it was concluded that the age, sex, albumin and creatinine, diabetes course were independently associated with the occurrence of DR. The above variables were used to establish BP-ANN model. The area under receiver operating characteristic curve (AUC) was significantly higher than that of MLR model (0.88 vs 0.74, P<0.05), the probability threshold of the model was 0.3. Type 2 diabetes mellitus (T2DM) were selected in our hospital, including 92 patients with DR (50.2%). The above BP-ANN model was used to predict the incidence of DR, and the AUC area was significantly higher than that of the MLR model (0.77 vs 0.70, P<0.05), the probability threshold was 0.7. CONCLUSION We established the BP-ANN model and applied it to diagnose DR. Taking diabetic course, age, sex, albumin and creatinine as the inputs of BP-ANN, the existence of DR could be well predicted. Meanwhile, the generalization ability of the model could be improved by selecting different probability thresholds in different ROC curves.
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Affiliation(s)
- Bo Su
- Department of Endocrinology, Aviation General Hospital, Beijing, 100012, People's Republic of China
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Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6662779. [PMID: 33727951 PMCID: PMC7937476 DOI: 10.1155/2021/6662779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/08/2023]
Abstract
Introduction A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.
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Cross-validating models of continuous data from simulation and experiment by using linear regression and artificial neural networks. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100457] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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Liu Q, Li Z, Ji Y, Martinez L, Zia UH, Javaid A, Lu W, Wang J. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infect Drug Resist 2019; 12:2311-2322. [PMID: 31440067 PMCID: PMC6666376 DOI: 10.2147/idr.s207809] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 07/06/2019] [Indexed: 01/26/2023] Open
Abstract
Objective Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China. Methods We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect. Results During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)12 and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)12 model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)12 model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model. Conclusion Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.
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Affiliation(s)
- Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.,Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, People's Republic of China
| | - Zhongqi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Ye Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Leonardo Martinez
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ui Haq Zia
- Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Arshad Javaid
- Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Wei Lu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, People's Republic of China
| | - Jianming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
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Zhang X, Lee S, Luo H, Liu H. A prediction model of sleep disturbances among female nurses by using the BP‐ANN. J Nurs Manag 2019; 27:1123-1130. [DOI: 10.1111/jonm.12782] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 03/14/2019] [Accepted: 04/15/2019] [Indexed: 12/13/2022]
Affiliation(s)
- Xin Zhang
- Faculty of School of Nursing, Peking Union Medical College Chinese Academy of Medical Sciences Beijing China
| | - Shih‐Yu Lee
- Faculty of School of Nursing University of Texas at Tyler Tyler Texas
| | - Honghui Luo
- Faculty of Medical Examination Center Peking Union Medical College Hospital Beijing China
| | - Huaping Liu
- Director of WHO Collaborating Center for Nursing Policy‐Making and Quality Management Peking Union Medical College, Chinese Academy of Medical Sciences Beijing China
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Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res 2018; 20:e10775. [PMID: 29848472 PMCID: PMC6000484 DOI: 10.2196/10775] [Citation(s) in RCA: 176] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/15/2018] [Accepted: 05/15/2018] [Indexed: 01/03/2023] Open
Abstract
Background Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.
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Affiliation(s)
- Ivan Contreras
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain
| | - Josep Vehi
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain.,Centro de Investigación Biomédica en Red de Diabetes y Enfermadades Metabólicas Asociadas, Girona, Spain
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Ma J, Yu J, Hao G, Wang D, Sun Y, Lu J, Cao H, Lin F. Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model. Lipids Health Dis 2017; 16:42. [PMID: 28219431 PMCID: PMC5319080 DOI: 10.1186/s12944-017-0434-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 02/14/2017] [Indexed: 11/25/2022] Open
Abstract
Background The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. Methods A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. Results The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. Conclusions In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people. Electronic supplementary material The online version of this article (doi:10.1186/s12944-017-0434-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jing Ma
- Department of Laboratory Medicine, First Affiliated Hospital, College of Medicine, Zhejiang University, Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China
| | - Jiong Yu
- The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, College of Medicine, Zhejiang University, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Guangshu Hao
- Key Laboratory for Laboratory Medicine of Ministry of Education, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Dan Wang
- Key Laboratory for Laboratory Medicine of Ministry of Education, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yanni Sun
- Key Laboratory for Laboratory Medicine of Ministry of Education, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jianxin Lu
- Key Laboratory for Laboratory Medicine of Ministry of Education, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Hongcui Cao
- The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, College of Medicine, Zhejiang University, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, 79 Qingchun Rd., Hangzhou City, 310003, China. .,Key Laboratory for Laboratory Medicine of Ministry of Education, Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Feiyan Lin
- Central laboratory, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang street, Ouhai District, Wenzhou, 325000, China.
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Hao G, Wang D, Sun Y, Yu J, Lin F, Cao H. Association of blood glucose and lipid levels with complete blood count indices to establish a regression model. Biomed Rep 2017; 6:339-345. [PMID: 28451397 DOI: 10.3892/br.2017.852] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 11/23/2016] [Indexed: 11/06/2022] Open
Abstract
Hyperglycemia and hyperlipidemia, which are usually diagnosed by analysis of blood glucose (GLU) and lipid levels, are two of the most common diseases in modern society. The purpose of the current study was to investigate the potential association between blood GLU and lipid levels with complete blood count (CBC) indices in overweight and healthy individuals and establish a regression model. There were 456 healthy and 421 overweight participants in the study. Data were collected on triglyceride (TG), total cholesterol (CHO), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood GLU and CBC. The distribution characteristics and differences between healthy and overweight subjects were analyzed. Subsequently, the associations between TG, CHO, HDL, LDL and GLU with CBC were analyzed using correlation analysis and multiple linear regression (MLR). Significant differences were identified between the healthy and overweight individuals in TG, CHO, HDL, LDL, GLU and in the majority of the CBC indices. The correlation analysis indicated that there were strong correlations between TG, LDL, HDL, CHO and GLU with CBC indices in the healthy and overweight subjects. The MLR demonstrated that the regression models of TG, LDL, HDL and CHO, but no GLU, were statistically significant in the two groups (P<0.001). The HDL regression model exhibited the best regression parameters; the multiple correlation coefficients (R) were 0.351 and 0.308 in the healthy and overweight subjects, respectively. In the overweight and healthy subjects, there were strong correlations between TG, LDL, HDL and CHO with CBC indices, with HDL being the most relevant to the CBC indices. The CBC demonstrated statistical significance in the diagnosis of hyperlipidemia.
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Affiliation(s)
- Guangshu Hao
- Central Laboratory, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China
| | - Dan Wang
- Central Laboratory, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China
| | - Yanni Sun
- Central Laboratory, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China
| | - Jiong Yu
- The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, P.R. China
| | - Feiyan Lin
- Central Laboratory, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China
| | - Hongcui Cao
- Central Laboratory, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China.,The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, P.R. China
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