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Fu S, Chen L, Lin H, Jiang X, Zhang S, Zhong F, Liu D. Prediction Model for Delayed Behavior of Early Ambulation After Surgery for Varicose Veins of the Lower Extremity: A Prospective Case-Control Study. Arch Phys Med Rehabil 2024:S0003-9993(24)01064-5. [PMID: 38909739 DOI: 10.1016/j.apmr.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 05/24/2024] [Accepted: 06/04/2024] [Indexed: 06/25/2024]
Abstract
OBJECTIVE To analyze influencing factors and establish a prediction model for delayed behavior of early ambulation after surgery for varicose veins of the lower extremity (VVLE). DESIGN A prospective case-control study. SETTING Patients with VVLE were recruited from 2 local hospitals. PARTICIPANTS In total, 498 patients with VVLE were selected using convenience sampling and divided into a training set and a test set. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES We collected information from the selected participants before surgery and followed up until the day after surgery, then divided them into a normal and delayed ambulation group. Propensity score matching was applied to all participants by type of surgery and anesthesia. All the characteristics in the 2 groups were compared using logistic regression, back propagation neural network (BPNN), and decision tree models. The accuracy, sensitivity, specificity, and area under the curve (AUC) values of the 3 models were compared to determine the optimal model. RESULTS A total of 406 participants were included after propensity score matching. The AUC values for the training sets of logistic regression, BPNN, and decision tree models were 0.850, 0.932, and 0.757, respectively. The AUC values for the test sets were 0.928, 0.984, and 0.776, respectively. A BPNN was the optimal model. Social Support Rating Scale score, preoperative 30-second sit-stand test score, Clinical-Etiology-Anatomy-Pathophysiology (CEAP) grade, Medical Coping Modes Questionnaire score, and whether you know the need for early ambulation, in descending order of the result of a BPNN model. A probability value greater than 0.56 indicated delayed behavior of early ambulation. CONCLUSIONS Clinicians should pay more attention to those with lower Social Support Rating Scale scores, poor lower limb strength, a higher CEAP grade, and poor medical coping ability, and make patients aware of the necessity and importance of early ambulation, thereby assisting decision-making regarding postoperative rehabilitation. Further research is needed to improve the method, add more variables, and transform the model into a scale to screen and intervene in the delayed behavior of early ambulation of VVLE in advance.
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Affiliation(s)
- Shuiqin Fu
- The School of Nursing, Fujian Medical University, China; Department of Surgery, The Second Affiliated Hospital of Xiamen Medical College, China
| | - Lanzhen Chen
- Department of Nursing, The Second Affiliated Hospital of Xiamen Medical College, China
| | - Hairong Lin
- Department of Nursing, The Second Affiliated Hospital of Xiamen Medical College, China
| | - Xiaoxiang Jiang
- Intensive Care Unit, The Second Affiliated Hospital of Xiamen Medical College, China
| | - Suzhen Zhang
- Department of General surgery, Zhongshan Hospital Xiamen University, China
| | - Fuxiu Zhong
- Department of Surgery, Fujian Medical University Union Hospital, China
| | - Dun Liu
- The School of Nursing, Fujian Medical University, China.
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Chen Q, Liu Q, Li K, Gavotte L, Frutos R, Chen T. Tuberculosis Prevalence Trends from a Predictive Modelling Study - 10 High-Burden Countries, 1980-2035. China CDC Wkly 2024; 6:225-229. [PMID: 38633431 PMCID: PMC11018513 DOI: 10.46234/ccdcw2024.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 04/19/2024] Open
Abstract
What is already known about this topic? Given the challenges presented by drug-resistant strains of tuberculosis (TB) and the rising mobility of the population, achieving the objective of eradicating TB appears uncertain. What is added by this report? The examination of TB incidence trends in 10 high-burden countries (HBCs) indicated a steady rise in cases, with India and China jointly accounting for nearly 70% of the burden. Projections for the future show diverse trajectories in these countries, with potential difficulties in reaching the TB elimination target, especially in Nigeria, Congo, and South Africa. What are the implications for public health practice? The number of TB cases is on the rise. It is crucial to learn from successful strategies to improve TB prevention and control worldwide through collaborative efforts.
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Affiliation(s)
- Qiuping Chen
- CIRAD, Intertryp, Montpellier, France
- Université de Montpellier, Montpellier, France
| | - Qiao Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen City, Fujian Province, China
| | - Kangguo Li
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | | | | | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen City, Fujian Province, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
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Wang Y, Wang G, Zhao Y, Wang C, Chen C, Ding Y, Lin J, You J, Gao S, Pang X. A deep learning model for predicting multidrug-resistant organism infection in critically ill patients. J Intensive Care 2023; 11:49. [PMID: 37941079 PMCID: PMC10633993 DOI: 10.1186/s40560-023-00695-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients. METHODS This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively. CONCLUSIONS This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.
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Affiliation(s)
- Yaxi Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Gang Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Yuxiao Zhao
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Cheng Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Chen Chen
- School of Nursing, Qingdao University, No. 38 Dengzhou Road, Qingdao, 266021, China
| | - Yaoyao Ding
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Jing Lin
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Jingjing You
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Silong Gao
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
| | - Xufeng Pang
- Department of Hospital-Acquired Infection Control, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
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Zhao D. Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China. IRANIAN JOURNAL OF PUBLIC HEALTH 2023; 52:2063-2072. [PMID: 37899935 PMCID: PMC10612558 DOI: 10.18502/ijph.v52i10.13844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/18/2022] [Indexed: 10/31/2023]
Abstract
Background Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control. Methods Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error). Results The optimum ES model was Brown's linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively. Conclusion The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.
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Affiliation(s)
- Daren Zhao
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China
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Lu N, Chen J, Rao Z, Guo B, Xu Y. Recent Advances of Biosensors for Detection of Multiple Antibiotics. BIOSENSORS 2023; 13:850. [PMID: 37754084 PMCID: PMC10526323 DOI: 10.3390/bios13090850] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023]
Abstract
The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.
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Affiliation(s)
| | | | | | | | - Ying Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
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Liu X, Jiang Q, Wu P, Han L, Zhou P. Global incidence, prevalence and disease burden of silicosis: 30 years' overview and forecasted trends. BMC Public Health 2023; 23:1366. [PMID: 37461046 DOI: 10.1186/s12889-023-16295-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/12/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Globally, silicosis accounts for 90% of all pneumoconiosis cases and is a serious public health issue. It is characterized by progressive inflammation and irreversible pulmonary fibrosis. A comprehensive analysis at temporal, spatial and population levels with the most updated data from GBD 2019 is provided in this study to estimate the disease burden of silicosis from 1990 to 2019 and make predictions to 2029. METHODS We delineated silicosis data on incidence, prevalence, and disability-adjusted life years (DALYs) as well as age-standardized rates (ASRs) across 30 years from GBD 2019. Joinpoint regression analysis was employed to detect temporal changes and estimate annual percentage change (APC) of each trend segment. Measures were stratified by time, location, age, and sociodemographic index (SDI). Back propagation artificial neural network (BP-ANN) model was applied to elaborate ASR trends from 1990 to 2019 and projections to the next 10 years. RESULTS Globally, silicosis incident, prevalent cases, and DALYs increased by 64.6%, 91.4%, and 20.8%, respectively. However, all the corresponding ASRs showed overall downward trends with an estimated average APC (AAPC) of -0.5(-0.7 to -0.3), -0.2(-0.5 to 0.0), and - 2.0(-2.2 to -1.8), respectively. Middle and high-middle SDI regions carried the heaviest disease burden. The highest disease burden of silicosis was mainly transferred to the older from 1990 to 2019. The trend of ASRs demonstrated a rapid decline between 2005 and 2019, followed by a continuous decline until 2029. CONCLUSION Though disease burden of silicosis has been on a decline in general from 1990 to 2019, which shows a promising prospect but cannot be ignored. We should pay more attention to implementing preventive tactics and improving the life quality of present sufferers.
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Affiliation(s)
- Xin Liu
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
- Jiangsu Province Engineering Research Center of Health Emergency, Nanjing, China
| | - Qingtao Jiang
- Department of clinical medicine, Jiangsu Health Vocational College, Nanjing, China
| | - Peihong Wu
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
- Jiangsu Province Engineering Research Center of Health Emergency, Nanjing, China
| | - Lei Han
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
- Jiangsu Province Engineering Research Center of Health Emergency, Nanjing, China.
| | - Peng Zhou
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
- Jiangsu Province Engineering Research Center of Health Emergency, Nanjing, China.
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Mo Z, Lu Z, Tang X, Lin X, Wang S, Zhang Y, Huang Z. Construction and evaluation of prognostic models of ECMO in elderly patients with cardiogenic shock based on BP neural network, random forest, and decision tree. Am J Transl Res 2023; 15:4639-4648. [PMID: 37560218 PMCID: PMC10408512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/29/2023] [Indexed: 08/11/2023]
Abstract
OBJECTIVE To analyze the predictive effect of a back propagation (BP) neural network, random forest (RF) and decision tree model on the prognosis of elderly patients with cardiogenic shock after extracorporeal membrane oxygenation (ECMO). METHODS This is a retrospective analysis of the clinical data of elderly patients with cardiogenic shock (258 cases) who underwent ECMO in People's Hospital of Guangxi Zhuang Autonomous Region from January 2016 to January 2022. All patients were followed up for 6 months after ECMO treatment. The prognosis was evaluated, and the prognostic factors were analyzed. BP neural network, RF and decision tree were used to establish predictive models, and the predictive performance of the models was evaluated. RESULTS Among the 258 elderly patients with cardiogenic shock, 52 (20.16%) died 6 months after the ECMO treatment. Based on BP neural network, RF, and decision tree, predictive models for the prognosis and death of elderly patients with cardiogenic shock were constructed. A test set was used to predict the performance of the three models. The results showed that the predictive performances of the three models were all more than 80.00%. The accuracy, sensitivity, and specificity of the RF model were 0.987, 1.000, and 0.929 respectively, which were higher than those of the decision tree model. The area under the receiver operating characteristic curve (AUC) of the RF model was 1.000, which was higher than 0.916 for the decision tree model. DeLong test showed that there was a significant difference in the AUC of the RF model compared to the decision tree test set (D=-2.063, P=0.042 < 0.05). CONCLUSION The predictive performance is good in all the three models, which have a high application value for prognosis of ECMO in elderly patients with cardiogenic shock. In clinical practice, predictive models should be selected according to the actual situation, so clinicians and patients can make decisions.
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Affiliation(s)
- Zucong Mo
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
| | - Zheng Lu
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
| | - Xiaogang Tang
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
| | - Xuezhen Lin
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
| | - Shuangquan Wang
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
| | - Yunli Zhang
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
| | - Zhai Huang
- Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China
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Pan H, Sun J, Luo X, Ai H, Zeng J, Shi R, Zhang A. A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management. Front Med (Lausanne) 2023; 10:1136653. [PMID: 37181375 PMCID: PMC10172657 DOI: 10.3389/fmed.2023.1136653] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/31/2023] [Indexed: 05/16/2023] Open
Abstract
Objective This study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management. Methods This multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screening methods. Logistic regression Model II built on the predictive factors in the previously released DR risk study was introduced into our current study to evaluate the model's effectiveness. Nine evaluation indicators were used to compare the performance of the two prediction models, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, balanced accuracy, calibration curve, Hosmer-Lemeshow test, and Net Reclassification Index (NRI). Results When including predictors, such as glycosylated hemoglobin A1c, disease course, postprandial blood glucose, age, systolic blood pressure, and albumin/urine creatinine ratio, multivariable logistic regression Model I demonstrated a better prediction ability than Model II. Model I revealed the highest AUROC (0.703), accuracy (0.796), precision (0.571), recall (0.035), F1 score (0.066), Hosmer-Lemeshow test (0.887), NRI (0.004), and balanced accuracy (0.514). Conclusion We have built an accurate DR risk prediction model with fewer indicators for patients with T2DM. It can be used to predict the individualized risk of DR in China effectively. In addition, the model can provide powerful auxiliary technical support for the clinical and health management of patients with diabetes comorbidities.
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Affiliation(s)
- Hong Pan
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jijia Sun
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luo
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Heling Ai
- Department of Public Utilities Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Zeng
- Department of Public Utilities Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rong Shi
- Department of Public Utilities Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Rong Shi,
| | - An Zhang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- An Zhang,
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Sarvestani SE, Hatam N, Seif M, Kasraian L, Lari FS, Bayati M. Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches. Sci Rep 2022; 12:22031. [PMID: 36539511 PMCID: PMC9767396 DOI: 10.1038/s41598-022-26461-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Providing fresh blood to keep people in need of blood alive, has always been a main issues of health systems. Right policy-making in this area requires accurate forecasting of blood demand. The current study aimed at predicting demand for different blood groups in Shiraz using Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and a hybrid approaches. In the current time series analysis, monthly data of the Shiraz hospitals and medical centers demand for 8 blood groups during 2012-2019 were gathered from Shiraz branch of Iranian Blood Transfusion Organization. ARIMA, ANN and a hybrid model of them was used for prediction. To validate and comprise ARIMA and ANN models, Mean Square Error (MSE) and Mean Absolute Error (MAE) criteria were used. Finally, ARIMA, ANN and hybrid model estimates were compared to actual data for the last 12 months. R3.6.3 were used for statistical analysis. Based on the MSE and MAE of models, ARIMA had the best prediction for demand of all blood groups except O+ and O-. Moreover, for most blood groups, ARIMA had closer prediction to actual data. The demand for four blood groups (mostly negative groups) was increasing and the demand for other four blood groups (mostly positive ones) was decreasing. All three approaches including ARIMA, ANN and the hybrid of them predicted an almost downward trend for the total blood demand. Differences in the performance of various models could be due to the reasons such as different forecast horizons, daily/month/annual data, different sample sizes, types of demand variables and the transformation applied on them, and finally different blood demand behaviors in communities. Advances in surgical techniques, fetal screening, reduction of accidents leading to heavy bleeding, and the modified pattern of blood request for surgeries appeared to have been effective in reducing the demand trend in the current study. However, a longer time period would certainly provide more accurate estimates.
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Affiliation(s)
- Seddigheh Edalat Sarvestani
- grid.412571.40000 0000 8819 4698Student Research Committee, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nahid Hatam
- grid.412571.40000 0000 8819 4698Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Almas Building, Alley 29, Qasrodasht Ave, Shiraz, 71336-54361 Iran
| | - Mozhgan Seif
- grid.412571.40000 0000 8819 4698Department of Epidemiology, School of Health, Non-Communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Leila Kasraian
- grid.418552.fBlood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran ,Shiraz Blood Transfusion Center, Shiraz, Iran
| | - Fazilat Sharifi Lari
- grid.412571.40000 0000 8819 4698Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Almas Building, Alley 29, Qasrodasht Ave, Shiraz, 71336-54361 Iran
| | - Mohsen Bayati
- grid.412571.40000 0000 8819 4698Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Almas Building, Alley 29, Qasrodasht Ave, Shiraz, 71336-54361 Iran
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Lou HR, Wang X, Gao Y, Zeng Q. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China. BMC Public Health 2022; 22:2167. [PMID: 36434563 PMCID: PMC9694549 DOI: 10.1186/s12889-022-14642-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model and multivariate Long Short-Term Memory Neural Network (LSTM) models. METHODS Disability adjusted life year (DALY) was used to evaluate the disease burden of occupational pneumoconiosis. ARIMA model, DNN model and multivariate LSTM model were used to establish prediction model. Three performance evaluation metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the prediction effects of the three models. RESULTS From 1990 to 2021, there were 10,694 cases of pneumoconiosis patients in Tianjin, resulting in a total of 112,725.52 person-years of DALY. During this period, the annual DALY showed a fluctuating trend, but it had a strong correlation with the number of pneumoconiosis patients, the average age of onset, the average age of receiving dust and the gross industrial product, and had a significant nonlinear relationship with them. The comparison of prediction results showed that the performance of multivariate LSTM model and DNN model is much better than that of traditional ARIMA model. Compared with the DNN model, the multivariate LSTM model performed better in the training set, showing lower RMES (42.30 vs. 380.96), MAE (29.53 vs. 231.20) and MAPE (1.63% vs. 2.93%), but performed less stable than the DNN on the test set, showing slightly higher RMSE (1309.14 vs. 656.44), MAE (886.98 vs. 594.47) and MAPE (36.86% vs. 22.43%). CONCLUSION The machine learning techniques of DNN and LSTM are an innovative method to accurately and efficiently predict the burden of pneumoconiosis with the simplest data. It has great application prospects in the monitoring and early warning system of occupational disease burden.
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Affiliation(s)
- He-Ren Lou
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China ,grid.265021.20000 0000 9792 1228School of Public Health, Tianjin Medical University, Tianjin, 300070 China
| | - Xin Wang
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China
| | - Ya Gao
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China
| | - Qiang Zeng
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China
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Chen Y, He J, Wang M. A hybrid of long short-term memory neural network and autoregressive integrated moving average model in forecasting HIV incidence and morality of post-neonatal population in East Asia: global burden of diseases 2000-2019. BMC Public Health 2022; 22:1938. [PMID: 36261815 PMCID: PMC9580197 DOI: 10.1186/s12889-022-14321-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 09/26/2022] [Indexed: 11/10/2022] Open
Abstract
Background To forecast the human immunodeficiency virus (HIV) incidence and mortality of post-neonatal population in East Asia including North Korea, South Korea, Mongolia, Japan and China Mainland and Taiwan province. Methods The data on the incidence and mortality of HIV in post-neonatal population from East Asia were obtained from the Global Burden of Diseases (GBD). The morbidity and mortality of post-neonatal HIV population from GBD 2000 to GBD 2013 were applied as the training set and the morbidity and mortality from GBD 2014 to GBD 2019 were used as the testing set. The hybrid of ARIMA and LSTM model was used to construct the model for assessing the morbidity and mortality in the countries and territories of East Asia, and predicting the morbidity and mortality in the next 5 years. Results In North Korea, the incidence and mortality of HIV showed a rapid increase during 2000–2010 and a gradual decrease during 2010–2019. The incidence of HIV was predicted to be increased and the mortality was decreased. In South Korea, the incidence was increased during 2000–2010 and decreased during 2010–2019, while the mortality showed fluctuant trend. As predicted, the incidence of HIV in South Korea might be increased and the mortality might be decreased during 2020–2025. In Mongolia, the incidence and mortality were slowly decreased during 2000–2005, increased during 2005–2015, and rapidly decreased till 2019. The predicted incidence and mortality of HIV showed a decreased trend. As for Japan, the incidence of HIV was rapidly increased till 2010 and then decreased till 2015. The predicted incidence of HIV in Japan was gradually increased. The mortality of HIV in Japan was fluctuant during 2000–2019 and was slowly decreased as predicted. The incidence and mortality of HIV in Taiwan during 2000–2019 was increased on the whole. The predicted incidence of HIV during was stationary and the mortality was decreased. In terms of China Mainland, the incidence and mortality of HIV was fluctuant during 2000–2019. The predicted incidence of HIV in China Mainland was stationary while the mortality was rapidly decreased. Conclusion On the whole, the incidence of HIV combined with other diseases in post-neonatal population was increased before 2010 and then decreased during 2010–2019 while the mortality of those patients was decreased in East Asia.
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Affiliation(s)
- Ying Chen
- Respiratory Medicine Department, XiXi Hospital of HangZhou (Affiliated HangZhou XiXi Hospital, Zhe Jiang University School of Medicine), No.2 Hengbu Road, Liuxia Street, Xihu District, Hangzhou, 310000, Zhejiang Province, China
| | - Jiawen He
- Respiratory Medicine Department, XiXi Hospital of HangZhou (Affiliated HangZhou XiXi Hospital, Zhe Jiang University School of Medicine), No.2 Hengbu Road, Liuxia Street, Xihu District, Hangzhou, 310000, Zhejiang Province, China
| | - Meihua Wang
- Respiratory Medicine Department, XiXi Hospital of HangZhou (Affiliated HangZhou XiXi Hospital, Zhe Jiang University School of Medicine), No.2 Hengbu Road, Liuxia Street, Xihu District, Hangzhou, 310000, Zhejiang Province, China.
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Zhu Z, Zhu X, Zhan Y, Gu L, Chen L, Li X. Development and comparison of predictive models for sexually transmitted diseases-AIDS, gonorrhea, and syphilis in China, 2011-2021. Front Public Health 2022; 10:966813. [PMID: 36091532 PMCID: PMC9450018 DOI: 10.3389/fpubh.2022.966813] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 07/25/2022] [Indexed: 01/24/2023] Open
Abstract
Background Accurate incidence prediction of sexually transmitted diseases (STDs) is critical for early prevention and better government strategic planning. In this paper, four different forecasting models were presented to predict the incidence of AIDS, gonorrhea, and syphilis. Methods The annual percentage changes in the incidence of AIDS, gonorrhea, and syphilis were estimated by using joinpoint regression. The performance of four methods, namely, the autoregressive integrated moving average (ARIMA) model, Elman neural network (ERNN) model, ARIMA-ERNN hybrid model and long short-term memory (LSTM) model, were assessed and compared. For 1-year prediction, the collected data from 2011 to 2020 were used for modeling to predict the incidence in 2021. For 5-year prediction, the collected data from 2011 to 2016 were used for modeling to predict the incidence from 2017 to 2021. The performance was evaluated based on four indices: mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results The morbidities of AIDS and syphilis are on the rise, and the morbidity of gonorrhea has declined in recent years. The optimal ARIMA models were determined: ARIMA(2,1,2)(0,1,1)12, ARIMA(1,1,2)(0,1,2)12, and ARIMA(3,1,2)(1,1,2)12 for AIDS, gonorrhea, and syphilis 1-year prediction, respectively; ARIMA (2,1,2)(0,1,1)12, ARIMA(1,1,2)(0,1,2)12, and ARIMA(2,1,1)(0,1,0)12 for AIDS, gonorrhea and syphilis 5-year prediction, respectively. For 1-year prediction, the MAPEs of ARIMA, ERNN, ARIMA-ERNN, and LSTM for AIDS are 23.26, 20.24, 18.34, and 18.63, respectively; For gonorrhea, the MAPEs are 19.44, 18.03, 17.77, and 5.09, respectively; For syphilis, the MAPEs are 9.80, 9.55, 8.67, and 5.79, respectively. For 5-year prediction, the MAPEs of ARIMA, ERNN, ARIMA-ERNN, and LSTM for AIDS are 12.86, 23.54, 14.74, and 25.43, respectively; For gonorrhea, the MAPEs are 17.07, 17.95, 16.46, and 15.13, respectively; For syphilis, the MAPEs are 21.88, 24.00, 20.18 and 11.20, respectively. In general, the performance ranking of the four models from high to low is LSTM, ARIMA-ERNN, ERNN, and ARIMA. Conclusion The time series predictive models show their powerful performance in forecasting STDs incidence and can be applied by relevant authorities in the prevention and control of STDs.
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Affiliation(s)
| | | | | | | | | | - Xiuyang Li
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Cinaglia P, Cannataro M. Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation. ENTROPY (BASEL, SWITZERLAND) 2022; 24:929. [PMID: 35885152 PMCID: PMC9322732 DOI: 10.3390/e24070929] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/25/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022]
Abstract
On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.
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Affiliation(s)
- Pietro Cinaglia
- Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy;
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Luo Z, Jia X, Bao J, Song Z, Zhu H, Liu M, Yang Y, Shi X. A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105910. [PMID: 35627447 PMCID: PMC9141474 DOI: 10.3390/ijerph19105910] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/07/2022] [Accepted: 05/10/2022] [Indexed: 01/27/2023]
Abstract
Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.
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Zhao D, Zhang H, Cao Q, Wang Z, He S, Zhou M, Zhang R. The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China. PLoS One 2022; 17:e0262734. [PMID: 35196309 PMCID: PMC8865644 DOI: 10.1371/journal.pone.0262734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/04/2022] [Indexed: 11/25/2022] Open
Abstract
Background and objective Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China. Methods The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy. Results There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models. Conclusions Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.
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Affiliation(s)
- Daren Zhao
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China
| | - Huiwu Zhang
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China
| | - Qing Cao
- Department of Medical Administration, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, P.R. China
| | - Zhiyi Wang
- Department of Medical Administration, Sichuan Cancer Hospital & Institute, Chengdu, Sichuan, P.R. China
| | - Sizhang He
- Department of Information and Statistics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P.R. China
| | - Minghua Zhou
- Department of Medical Administration, Luzhou People’s Hospital, Luzhou, Sichuan, P.R. China
| | - Ruihua Zhang
- School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, P.R. China
- * E-mail:
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Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1026978. [PMID: 34804136 PMCID: PMC8604606 DOI: 10.1155/2021/1026978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/18/2021] [Accepted: 10/26/2021] [Indexed: 12/04/2022]
Abstract
Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.
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Li G, Tan Z, Xu W, Xu F, Wang L, Chen J, Wu K. A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification. BMC Med Inform Decis Mak 2021; 21:99. [PMID: 34330266 PMCID: PMC8322832 DOI: 10.1186/s12911-021-01453-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 02/22/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What's more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease. METHODS In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension. RESULTS Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model. CONCLUSION In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
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Affiliation(s)
- Guixiang Li
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China.,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
| | - Zhongwei Tan
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Weikang Xu
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Fei Xu
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Lei Wang
- Department of Artificial Intelligence, College of Information and Communication Engineering, Hainan University, Haikou, 570228, China.
| | - Jun Chen
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China. .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China.
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China. .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China. .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China. .,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China. .,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China. .,Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, 510006, China. .,Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, 980-8575, Japan.
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Wang Y, Wang L, Sun Y, Wu M, Ma Y, Yang L, Meng C, Zhong L, Hossain MA, Peng B. Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network. BMC Public Health 2021; 21:991. [PMID: 34039329 PMCID: PMC8157412 DOI: 10.1186/s12889-021-11002-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 05/06/2021] [Indexed: 01/07/2023] Open
Abstract
Background Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. Methods We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model. Results The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur. Conclusions Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11002-5.
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Affiliation(s)
- Yuqi Wang
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Liangxu Wang
- School of Basic Medicine, Kunming Medical University, Kunming, 650031, China
| | - Yanli Sun
- The First Affiliated Hospital of Chongqing Medical University Health Management Center, Chongqing, 400016, China
| | - Miao Wu
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Yingjie Ma
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Lingping Yang
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Chun Meng
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Li Zhong
- The First Affiliated Hospital of Chongqing Medical University Health Management Center, Chongqing, 400016, China
| | - Mohammad Arman Hossain
- The First Affiliated Hospital of Chongqing Medical University, Department of Urology, Chongqing, 400016, China
| | - Bin Peng
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China.
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Ruan F, Ding X, Li H, Wang Y, Ye K, Kan H. Back propagation neural network model for medical expenses in patients with breast cancer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3690-3698. [PMID: 34198407 DOI: 10.3934/mbe.2021185] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Breast cancer seriously endangers women's life and health, and brings huge economic burden to the family and society. The aim of this study was to analyze the medical expenses and influencing factors of breast cancer patients, and provide theoretical basis for reasonable control of medical expenses of breast cancer patients. METHODS The medical expenses and related information of all female breast cancer patients diagnosed in our hospitals from 2017 to 2019 were collected. Through SSPS Clementine 12.0 software, the back propagation (BP) neural network model and multiple linear regression model were constructed respectively, and the influencing factors of medical expenses of breast cancer patients in the two models were compared. RESULTS In the study of medical expenses of breast cancer patients, the prediction error of BP neural network model is less than that of multiple linear regression model. At the same time, the results of the two models showed that the length of stay and region were the top two factors affecting the medical expenses of breast cancer patients. CONCLUSION Compared with multiple linear regression model, BP neural network model is more suitable for the analysis of medical expenses in patients with breast cancer.
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Affiliation(s)
- Feiyan Ruan
- School of Nursing, Anhui Medical University, Hefei 230032, China
- Breast surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Xiaotong Ding
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Huiping Li
- School of Nursing, Anhui Medical University, Hefei 230032, China
| | - Yixuan Wang
- School of Nursing, Anhui Medical University, Hefei 230032, China
| | - Kemin Ye
- School of Nursing, Anhui Medical University, Hefei 230032, China
| | - Houming Kan
- Pain department, SIR RUN RUN Hospital of Nanjing Medical University, Nanjing 211166, China
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