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Li HM, Zheng JX, Midzi N, Mutsaka-Makuvaza MJ, Lv S, Xia S, Qian YJ, Xiao N, Berguist R, Zhou XN. Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning. Infect Dis Model 2024; 9:1081-1094. [PMID: 38988829 PMCID: PMC11233785 DOI: 10.1016/j.idm.2024.06.001] [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: 11/19/2023] [Revised: 06/02/2024] [Accepted: 06/08/2024] [Indexed: 07/12/2024] Open
Abstract
Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R2). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S. mansoni infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher S. haematobium prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher S. mansoni prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R2 = 0.517 for S. haematobium, and with the corresponding figures for S. mansoni being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of S. haematobium at 13.8% and that of S. mansoni at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of S. haematobium 17.1% and that of S. mansoni prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to S. mansoni. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.
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Affiliation(s)
- Hong-Mei Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Jin-Xin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Nicholas Midzi
- National Institute of Health Research, Ministry of Health and Child Care, Harare, Zimbabwe
| | - Masceline Jenipher Mutsaka-Makuvaza
- National Institute of Health Research, Ministry of Health and Child Care, Harare, Zimbabwe
- University of Rwanda, College of Medicine and Health Sciences, School of Medicine and Pharmacy, Department of Microbiology and Parasitology, Rwanda
| | - Shan Lv
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Ying-Jun Qian
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Ning Xiao
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | | | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
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Sahin Demirel AN. Investigating the impact of climate variables on the organic honey yield in Turkey using XGBoost machine learning. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 39120002 DOI: 10.1002/jsfa.13806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/24/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The Turkish organic honey industry, a major player in the global market, faces challenges due to climate fluctuations. Understanding the influence of climate factors on honey production is vital for sustainable farming and economic stability. METHOD This study uses a machine learning approach with the XGBoost algorithm to analyze temperature, wind, humidity, precipitation and surface pressure over a 20-year period from 2004 to 2023. RESULTS The results show that these factors significantly impact organic honey production, with temperature, wind, humidity, precipitation and surface pressure having effects of 41.20%, 26.50%, 12.47%, 11.42% and 8.41%, respectively. Sensitivity analysis reveals the model's sensitivity to even minor fluctuations in these variables. CONCLUSION The results of this research underscore the necessity of integrating climate change mitigation and adaptation measures into agricultural policies and beekeeping practices. This study showcases the practical application of machine learning in deciphering the intricate relationship between climate change and the production of crops, emphasizing the importance of data-driven decision-making to guarantee long-term sustainability and financial stability in the sector. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Ayca Nur Sahin Demirel
- Department of Agricultural Economics, Faculty of Agriculture, Iğdur University, Iğdır, Turkey
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Grimm T, Branch A, Thompson KA, Salveson A, Zhao J, Johnson D, Hering AS, Newhart KB. Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process. ACS ES&T ENGINEERING 2024; 4:1492-1506. [PMID: 38899163 PMCID: PMC11184555 DOI: 10.1021/acsestengg.4c00042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.
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Affiliation(s)
- Taylor
R. Grimm
- Department
of Statistical Science, Baylor University, Waco, Texas 76798, United States
| | - Amos Branch
- Carollo
Engineers, Inc., Walnut Creek, California 94598, United States
| | - Kyle A. Thompson
- Carollo
Engineers, Inc., Walnut Creek, California 94598, United States
| | - Andrew Salveson
- Carollo
Engineers, Inc., Walnut Creek, California 94598, United States
| | - John Zhao
- Las
Virgenes Municipal Water District, Calabasas, California 91302, United States
| | - Darrell Johnson
- Las
Virgenes Municipal Water District, Calabasas, California 91302, United States
| | - Amanda S. Hering
- Department
of Statistical Science, Baylor University, Waco, Texas 76798, United States
| | - Kathryn B. Newhart
- Department
of Geography and Environmental Engineering, United States Military Academy, West Point, New York 10996, United States
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Jiang S, Li B, Yang Z, Li Y, Zhou Z. A back propagation neural network based respiratory motion modelling method. Int J Med Robot 2024; 20:e2647. [PMID: 38804195 DOI: 10.1002/rcs.2647] [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: 02/21/2024] [Revised: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases. METHODS Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement. RESULTS The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm. CONCLUSIONS The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.
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Affiliation(s)
- Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Bowen Li
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Yuhua Li
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Zeyang Zhou
- School of Mechanical Engineering, Tianjin University, Tianjin, China
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Hooda S, Mondal P. Predictive modeling of plastic pyrolysis process for the evaluation of activation energy: Explainable artificial intelligence based comprehensive insights. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121189. [PMID: 38759553 DOI: 10.1016/j.jenvman.2024.121189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/30/2024] [Accepted: 05/13/2024] [Indexed: 05/19/2024]
Abstract
Pyrolysis, a thermochemical conversion approach of transforming plastic waste to energy has tremendous potential to manage the exponentially increasing plastic waste. However, understanding the process kinetics is fundamental to engineering a sustainable process. Conventional analysis techniques do not provide insights into the influence of characteristics of feedstock on the process kinetics. Present study exemplifies the efficacy of using machine learning for predictive modeling of pyrolysis of waste plastics to understand the complexities of the interrelations of predictor variables and their influence on activation energy. The activation energy for pyrolysis of waste plastics was evaluated using machine learning models namely Random Forest, XGBoost, CatBoost, and AdaBoost regression models. Feature selection based on the multicollinearity of data and hyperparameter tuning of the models utilizing RandomizedSearchCV was conducted. Random forest model outperformed the other models with coefficient of determination (R2) value of 0.941, root mean square error (RMSE) value of 14.69 and mean absolute error (MAE) value of 8.66 for the testing dataset. The explainable artificial intelligence-based feature importance plot and the summary plot of the shapely additive explanations projected fixed carbon content, ash content, conversion value, and carbon content as significant parameters of the model in the order; fixed carbon > carbon > ash content > degree of conversion. Present study highlighted the potential of machine learning as a powerful tool to understand the influence of the characteristics of plastic waste and the degree of conversion on the activation energy of a process that is essential for designing the large-scale operations and future scale-up of the process.
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Affiliation(s)
- Sanjeevani Hooda
- Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
| | - Prasenjit Mondal
- Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
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Mao Y, Hu Z, Li H, Zheng H, Yang S, Yu W, Tang B, Yang H, He R, Guo W, Ye K, Yang A, Zhang S. Recent advances in microplastic removal from drinking water by coagulation: Removal mechanisms and influencing factors. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 349:123863. [PMID: 38565391 DOI: 10.1016/j.envpol.2024.123863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/26/2024] [Accepted: 03/23/2024] [Indexed: 04/04/2024]
Abstract
Microplastics (MPs) are emerging contaminants that are widely detected in drinking water and pose a potential risk to humans. Therefore, the MP removal from drinking water is a critical challenge. Recent studies have shown that MPs can be removed by coagulation. However, the coagulation removal of MPs from drinking water remains inadequately understood. Herein, the efficiency, mechanisms, and influencing factors of coagulation for removing MPs from drinking water are critically reviewed. First, the efficiency of MP removal by coagulation in drinking water treatment plants (DWTPs) and laboratories was comprehensively summarized, which indicated that coagulation plays an important role in MP removal from drinking water. The difference in removal effectiveness between the DWTPs and laboratory was mainly due to variations in treatment conditions and limitations of the detection techniques. Several dominant coagulation mechanisms for removing MPs and their research methods are thoroughly discussed. Charge neutralization is more relevant for small-sized MPs, whereas large-sized MPs are more dependent on adsorption bridging and sweeping. Furthermore, the factors influencing the efficiency of MP removal were jointly analyzed using meta-analysis and a random forest model. The meta-analysis was used to quantify the individual effects of each factor on coagulation removal efficiency by performing subgroup analysis. The random forest model quantified the relative importance of the influencing factors on removal efficiency, the results of which were ordered as follows: MPs shape > Coagulant type > Coagulant dosage > MPs concentration > MPs size > MPs type > pH. Finally, knowledge gaps and potential future directions are proposed. This review assists in the understanding of the coagulation removal of MPs, and provides novel insight into the challenges posed by MPs in drinking water.
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Affiliation(s)
- Yufeng Mao
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China; Key Laboratory of Eco-Environment of Three Gorges Region, Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Zuoyuan Hu
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Hong Li
- Key Laboratory of Eco-Environment of Three Gorges Region, Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Huaili Zheng
- Key Laboratory of Eco-Environment of Three Gorges Region, Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Shengfa Yang
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Weiwei Yu
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Bingran Tang
- Key Laboratory of Eco-Environment of Three Gorges Region, Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Hao Yang
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Ruixu He
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Wenshu Guo
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Kailai Ye
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Aoguang Yang
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Shixin Zhang
- Key Laboratory of Hydraulic and Waterway Engineering, Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China.
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Grdinic AG, Radovanovic S, Gleditsch J, Jørgensen CT, Asady E, Pettersen HH, Delibasic B, Ghanima W. Developing a machine learning model for bleeding prediction in patients with cancer-associated thrombosis receiving anticoagulation therapy. J Thromb Haemost 2024; 22:1094-1104. [PMID: 38184201 DOI: 10.1016/j.jtha.2023.12.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Only 1 conventional score is available for assessing bleeding risk in patients with cancer-associated thrombosis (CAT): the CAT-BLEED score. OBJECTIVES Our aim was to develop a machine learning-based risk assessment model for predicting bleeding in CAT and to evaluate its predictive performance in comparison to that of the CAT-BLEED score. METHODS We collected 488 attributes (clinical data, biochemistry, and International Classification of Diseases, 10th Revision, diagnosis) in 1080 unique patients with CAT. We compared CAT-BLEED score, Ridge and Lasso logistic regression, random forest, and Extreme Gradient Boosting (XGBoost) algorithms for predicting major bleeding or clinically relevant nonmajor bleeding occurring 1 to 90 days, 1 to 365 days, and 90 to 455 days after venous thromboembolism (VTE). RESULTS The predictive performances of Lasso logistic regression, random forest, and XGBoost were higher than that of the CAT-BLEED score in the prediction of bleeding occurring 1 to 90 days and 1 to 365 days after VTE. For predicting major bleeding or clinically relevant nonmajor bleeding 1 to 90 days after VTE, the CAT-BLEED score achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.48 ± 0.13, while Lasso logistic regression and XGBoost both achieved AUROCs of 0.64 ± 0.12. For predicting bleeding 1 to 365 days after VTE, the CAT-BLEED score achieved a mean AUROC of 0.47 ± 0.08, while Lasso logistic regression and XGBoost achieved AUROCs of 0.64 ± 0.08 and 0.59 ± 0.08, respectively. CONCLUSION This is the first machine learning-based risk model for bleeding prediction in patients with CAT receiving anticoagulation therapy. Its predictive performance was higher than that of the conventional CAT-BLEED score. With further development, this novel algorithm might enable clinicians to perform personalized anticoagulation strategies with improved clinical outcomes.
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Affiliation(s)
- Aleksandra G Grdinic
- Department of Cardiology, Østfold Hospital, Sarpsborg, Norway; Department of Research, Østfold Hospital, Sarpsborg, Norway.
| | - Sandro Radovanovic
- Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia
| | - Jostein Gleditsch
- Department of Radiology, Østfold Hospital, Sarpsborg, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Camilla Tøvik Jørgensen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Emergency Medicine, Østfold Hospital, Sarpsborg, Norway
| | - Elia Asady
- Department of Research, Østfold Hospital, Sarpsborg, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Boris Delibasic
- Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia
| | - Waleed Ghanima
- Department of Research, Østfold Hospital, Sarpsborg, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Hematology, Oslo University Hospital, Oslo, Norway
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Nishan A, M. Taslim Uddin Raju S, Hossain MI, Dipto SA, M. Tanvir Uddin S, Sijan A, Chowdhury MAS, Ahmad A, Mahamudul Hasan Khan M. A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms. Heliyon 2024; 10:e27779. [PMID: 38533045 PMCID: PMC10963242 DOI: 10.1016/j.heliyon.2024.e27779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
Background and objective Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. Methods In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. Results The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. Conclusion The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.
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Affiliation(s)
- Araf Nishan
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - S. M. Taslim Uddin Raju
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Md Imran Hossain
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Safin Ahmed Dipto
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - S. M. Tanvir Uddin
- Department of Electrical and Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh
| | - Asif Sijan
- Department of Software Engineering, American International University, Dhaka, Bangladesh
| | - Md Abu Shahid Chowdhury
- Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Ashfaq Ahmad
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
| | - Md Mahamudul Hasan Khan
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh
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Liu L, Jiang J, Wu L, Zeng DM, Yan C, Liang L, Shi J, Xie Q. Assessing the risk of concurrent mycoplasma pneumoniae pneumonia in children with tracheobronchial tuberculosis: retrospective study. PeerJ 2024; 12:e17164. [PMID: 38560467 PMCID: PMC10979740 DOI: 10.7717/peerj.17164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/06/2024] [Indexed: 04/04/2024] Open
Abstract
Objective This study aimed to create a predictive model based on machine learning to identify the risk for tracheobronchial tuberculosis (TBTB) occurring alongside Mycoplasma pneumoniae pneumonia in pediatric patients. Methods Clinical data from 212 pediatric patients were examined in this retrospective analysis. This cohort included 42 individuals diagnosed with TBTB and Mycoplasma pneumoniae pneumonia (combined group) and 170 patients diagnosed with lobar pneumonia alone (pneumonia group). Three predictive models, namely XGBoost, decision tree, and logistic regression, were constructed, and their performances were assessed using the receiver's operating characteristic (ROC) curve, precision-recall curve (PR), and decision curve analysis (DCA). The dataset was divided into a 7:3 ratio to test the first and second groups, utilizing them to validate the XGBoost model and to construct the nomogram model. Results The XGBoost highlighted eight significant signatures, while the decision tree and logistic regression models identified six and five signatures, respectively. The ROC analysis revealed an area under the curve (AUC) of 0.996 for XGBoost, significantly outperforming the other models (p < 0.05). Similarly, the PR curve demonstrated the superior predictive capability of XGBoost. DCA further confirmed that XGBoost offered the highest AIC (43.226), the highest average net benefit (0.764), and the best model fit. Validation efforts confirmed the robustness of the findings, with the validation groups 1 and 2 showing ROC and PR curves with AUC of 0.997, indicating a high net benefit. The nomogram model was shown to possess significant clinical value. Conclusion Compared to machine learning approaches, the XGBoost model demonstrated superior predictive efficacy in identifying pediatric patients at risk of concurrent TBTB and Mycoplasma pneumoniae pneumonia. The model's identification of critical signatures provides valuable insights into the pathogenesis of these conditions.
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Affiliation(s)
- Lin Liu
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Jie Jiang
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Lei Wu
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - De miao Zeng
- Department of Joint Surgery, he Hong-he Affiliated Hospital of Kunming Medical University/The Southern Central Hospital of Yun-nan Province (The First People’s Hospital of Honghe State), Changsha, Hunan, China
| | - Can Yan
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Linlong Liang
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Jiayun Shi
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Qifang Xie
- Department of Pediatrics, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
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Wang C, Wang L, Yu H, Seo A, Wang Z, Rajabzadeh S, Ni BJ, Shon HK. Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration. CHEMOSPHERE 2024; 350:140999. [PMID: 38151066 DOI: 10.1016/j.chemosphere.2023.140999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 12/29/2023]
Abstract
In this study, machine learning-based models were established for layer-by-layer (LBL) nanofiltration (NF) membrane performance prediction and polymer candidate exploration. Four different models, i.e., linear, random forest (RF), boosted tree (BT), and eXtreme Gradient Boosting (XGBoost), were formed, and membrane performance prediction was determined in terms of membrane permeability and selectivity. The XGBoost exhibited optimal prediction accuracy for membrane permeability (coefficient of determination (R2): 0.99) and membrane selectivity (R2: 0.80). The Shapley Additive exPlanation (SHAP) method was utilized to evaluate the effects of different LBL NF membrane fabrication conditions on membrane performances. The SHAP method was also used to identify the relationships between polymer structure and membrane performance. Polymers were represented by Morgan fingerprint, which is an effective description approach for developing modeling. Based on the SHAP value results, two reference Morgan fingerprints were constructed containing atomic groups with positive contributions to membrane permeability and selectivity. According to the reference Morgan fingerprint, 204 potential polymers were explored from the largest polymer database (PoLyInfo). By calculating the similarities between each potential polymer and both reference Morgan fingerprints, 23 polymer candidates were selected and could be further used for LBL NF membrane fabrication with the potential for providing good membrane performance. Overall, this work provided new ways both for LBL NF membrane performance prediction and high-performance polymer candidate exploration. The source code for the models and algorithms used in this study is publicly available to facilitate replication and further research. https://github.com/wangliwfsd/LLNMPP/.
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Affiliation(s)
- Chen Wang
- School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, 2007, Australia
| | - Li Wang
- CSIRO Space and Astronomy, PO Box 1130, Bentley, WA, 6102, Australia
| | - Hanwei Yu
- School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, 2007, Australia
| | - Allan Seo
- School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, 2007, Australia
| | - Zhining Wang
- Shandong Provincial Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao, 266237, China
| | - Saeid Rajabzadeh
- School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, 2007, Australia
| | - Bing-Jie Ni
- School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Ho Kyong Shon
- School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, 2007, Australia.
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11
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Ito H, Sakamaki K, Young GJ, Blair PS, Hashim H, Lane JA, Kobayashi K, Clout M, Abrams P, Chapple C, Malde S, Drake MJ. Predicting Prostate Surgery Outcomes from Standard Clinical Assessments of Lower Urinary Tract Symptoms To Derive Prognostic Symptom and Flowmetry Criteria. Eur Urol Focus 2024; 10:197-204. [PMID: 37455216 DOI: 10.1016/j.euf.2023.06.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/01/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Assessment of male lower urinary tract symptoms (LUTS) needs to identify predictors of symptom outcomes when interventional treatment is planned. OBJECTIVE To develop a novel prediction model for prostate surgery outcomes and validate it using a separate patient cohort and derive thresholds for key clinical parameters. DESIGN, SETTING, AND PARTICIPANTS From the UPSTREAM trial of 820 men seeking treatment for LUTS, analysis of bladder diary (BD), International Prostate Symptom Score (IPSS), IPSS-quality of life, and uroflowmetry data was performed for 176 participants who underwent prostate surgery and provided complete data. For external validation, data from a retrospective database of surgery outcomes in a Japanese urology department (n = 227) were used. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Symptom improvement was defined as a reduction in total IPSS of ≥3 points. Multiple logistic regression, classification tree analysis, and random forest models were generated, including versions with and without BD data. RESULTS AND LIMITATIONS Multiple logistic regression without BD data identified age (p = 0.029), total IPSS (p = 0.0016), and maximum flow rate (Qmax; p = 0.066) as predictors of outcomes, with area under the receiver operating characteristic curve (AUC) of 77.1%. Classification tree analysis without BD data gave thresholds of IPSS <16 and Qmax ≥13 ml/s (AUC 75.0%). The random forest model, which included all clinical parameters except BD data, had an AUC of 94.7%. Internal validation using the bootstrap method showed reasonable AUCs (69.6-85.8%). Analyses using BD data marginally improved the model fits. External validation gave comparable AUCs for logistic regression, classification tree analysis, and random forest models (all without BD; 70.9%, 67.3%, and 68.5%, respectively). Limitations include the significant number of men with incomplete baseline data and limited assessments in the external validation cohort. CONCLUSIONS Outcomes of prostate surgery can be predicted preoperatively using age, total IPSS, and uroflowmetry data, with prognostic thresholds of 16 for IPSS and 13 ml/s for Qmax. PATIENT SUMMARY This study identified key preoperative factors that can predict outcomes of prostate surgery for bothersome urinary symptoms, including which patients are at risk of a poor outcome.
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Affiliation(s)
- Hiroki Ito
- Bristol Urological Institute, North Bristol NHS Trust, Southmead Hospital, Bristol, UK; Department of Urology, Yokohama City University, Yokohama, Japan
| | - Kentaro Sakamaki
- Center for Data Science, Yokohama City University, Yokohama, Japan
| | - Grace J Young
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Bristol Trials Centre, Bristol Medical School, University of Bristol, Bristol, UK
| | - Peter S Blair
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Bristol Trials Centre, Bristol Medical School, University of Bristol, Bristol, UK
| | - Hashim Hashim
- Bristol Urological Institute, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - J Athene Lane
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Bristol Trials Centre, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kazuki Kobayashi
- Department of Urology, Yokosuka Kyosai Hospital, Yokosuka, Japan
| | - Madeleine Clout
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Bristol Trials Centre, Bristol Medical School, University of Bristol, Bristol, UK
| | - Paul Abrams
- Bristol Urological Institute, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - Christopher Chapple
- Sheffield Teaching Hospitals NHS Trust, Royal Hallamshire Hospital, Sheffield, UK
| | - Sachin Malde
- Urology Centre, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Marcus J Drake
- Bristol Urological Institute, North Bristol NHS Trust, Southmead Hospital, Bristol, UK; Department of Surgery and Cancer, Imperial College, London, UK.
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12
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Jin Z, Ma F, Chen H, Guo S. Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study. Sci Rep 2023; 13:22899. [PMID: 38129529 PMCID: PMC10739959 DOI: 10.1038/s41598-023-49925-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925-0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.
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Affiliation(s)
- Zhe Jin
- School of Medical Technology, Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
| | - Fengmei Ma
- Department of Otorhinolaryngology, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, 050011, People's Republic of China
| | - Haoyang Chen
- Medicine-Education Coordination and Medical Education Research Center, Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
| | - Shufan Guo
- Department of Otorhinolaryngology, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, 050011, People's Republic of China.
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13
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Ito H, Sakamaki K, Fukuda T, Yamamichi F, Watanabe T, Tabei T, Inoue T, Matsuzaki J, Kobayashi K. Models to predict the surgical outcome of mini-ECIRS (endoscopic combined intrarenal surgery) for renal and/or ureteral stones. Sci Rep 2023; 13:22848. [PMID: 38129560 PMCID: PMC10739798 DOI: 10.1038/s41598-023-50022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
To establish a safer and more efficient treatment strategy with mini-endoscopic combined intrarenal surgery (ECIRS), the present study aimed to develop models to predict the outcomes of mini-ECIRS in patients with renal and/or ureteral stones. We retrospectively analysed consecutive patients with renal and/or ureteral stones who underwent mini-ECIRS at three Japanese tertiary institutions. Final treatment outcome was evaluated by CT imaging at 1 month postoperatively and stone free (SF) was defined as completely no residual stone or residual stone fragments ≤ 2 mm. Three prognostic models (multiple logistic regression, classification tree analysis, and machine learning-based random forest) were developed to predict surgical outcomes using preoperative clinical factors. Clinical data from 1432 ECIRS were pooled from a database registered at three institutions, and 996 single sessions of mini-ECIRS were analysed in this study. The overall SF rate was 62.3%. The multiple logistic regression model consisted of stone burden (P < 0.001), number of involved calyces (P < 0.001), nephrostomy prior to mini-ECIRS (P = 0.091), and ECOG-PS (P = 0.110), wherein the area under the curve (AUC) was 70.7%. The classification tree analysis consisted of the number of involved calyces with an AUC of 61.7%. The random forest model showed that the top predictive variable was the number of calyces involved, with an AUC of 91.9%. Internal validation revealed that the AUCs for the multiple logistic regression model, classification tree analysis and random forest models were 70.4, 69.6 and 85.9%, respectively. The number of involved calyces, and a smaller stone burden implied a SF outcome. The machine learning-based model showed remarkably high accuracy and may be a promising tool for physicians and patients to obtain proper consent, avoid inefficient surgery, and decide preoperatively on the most efficient treatment strategies, including staged mini-ECIRS.
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Affiliation(s)
- Hiroki Ito
- Department of Urology, Yokosuka Kyosai Hospital, Yokosuka, Japan.
- Department of Urology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
| | - Kentaro Sakamaki
- Faculty of Health Data Science, Juntendo University, Tokyo, Japan
| | - Tetsuo Fukuda
- Department of Urology, Ohguchi East General Hospital, Yokohama, Japan
| | | | | | - Tadashi Tabei
- Department of Urology, Yokosuka Kyosai Hospital, Yokosuka, Japan
| | - Takaaki Inoue
- Department of Urology, Hara Genitourinary Hospital, Kobe, Japan
| | - Junichi Matsuzaki
- Department of Urology, Ohguchi East General Hospital, Yokohama, Japan
| | - Kazuki Kobayashi
- Department of Urology, Yokosuka Kyosai Hospital, Yokosuka, Japan
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Wang Y, Wei B, Zhao T, Shen H, Liu X, Wang J, Wang Q, Shen R, Feng D. Machine learning-based prediction models for parathyroid carcinoma using pre-surgery cognitive function and clinical features. Sci Rep 2023; 13:19007. [PMID: 37923800 PMCID: PMC10624903 DOI: 10.1038/s41598-023-46294-7] [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: 07/23/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023] Open
Abstract
Patients with parathyroid carcinoma (PC) are often diagnosed postoperatively, due to incomplete resection during the initial surgery, resulting in poor outcomes. The aim of our study was to investigate the pre-surgery indicators of PC and try to develop a predictive model for PC utilizing machine learning. Evaluation of pre-surgery neuropsychological function and confirmation of pathology were carried out in 133 patients with primary hyperparathyroidism in Beijing Chaoyang Hospital from December 2019 to January 2023. Patients were randomly divided into a training cohort (n = 93) and a validating cohort (n = 40). Analysis of the clinical dataset, two machine learning including the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression were utilized to develop the prediction model for PC. Logistic regression analysis was also conducted for comparison. Significant differences in elevated parathyroid hormone and decreased serum phosphorus in PC compared to (BP). The lower score of MMSE and MOCA was observed in PC and a cutoff of MMSE < 24 was the optimal threshold to stratify PC from BP (area under the curve AUC 0.699 vs 0.625). The predicted probability of PC by machine learning was similar to the observed probability in the test set, whereas the logistic model tended to overpredict the possibility of PC. The XGBoost model attained a higher AUC than the logistic algorithms and LASSO models. (0.835 vs 0.683 vs 0.607). Preoperative cognitive function may be a probable predictor for PC. The cognitive function-based prediction model based on the XGBoost algorithm outperformed LASSO and logistic regression, providing valuable preoperative assistance to surgeons in clinical decision-making for patients suspected PC.
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Affiliation(s)
- Yuting Wang
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Bojun Wei
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
| | - Teng Zhao
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Hong Shen
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jiacheng Wang
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Qian Wang
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Rongfang Shen
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Dalin Feng
- Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
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15
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Li Q, Yang H, Hao N, Du M, Zhao Y, Li Y, Li X. Biodegradability analysis of Dioxins through in silico methods: Model construction and mechanism analysis. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118898. [PMID: 37657295 DOI: 10.1016/j.jenvman.2023.118898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/18/2023] [Accepted: 08/27/2023] [Indexed: 09/03/2023]
Abstract
The biodegradation treatment of dioxins has long been of interest due to its good ecological and economic effects. In this study, the biodegradability of polychlorinated dibenzo-p-dioxins (PCDDs) were investigated by constructing machine learning and multiple linear regression models. The maximum chlorine atomic charge (qHirshfeldCl+), which characterizes the biodegradation ability of PCDDs, was used as the response value. The random forest model was used to rank the importance on the 1471 descriptors of PCDDs, and the BCUTp-1 h, QXZ, JGI4, ATSC8c, VE3_Dt, topoShape, and maxwHBa were screened as the important descriptors by Pearson's correlation coefficient method. A quantitative structure-activity relationship (QSAR) model was constructed to predict the biodegradability of PCDDs. In addition, the extreme gradient boosting (XGBoost) and random forest model were also constructed and proved the good predictability of QSAR model. The biodegradability of polychlorinated dibenzofurans (PCDFs) can also be predicted by the constructed three models from a certain level after adjusting some model parameters, which further proved the versatility of the models. Besides, the sensitivity analysis of the QSAR model and a 3D-QSAR model was developed to investigate the biodegradability mechanisms of PCDDs. Results showed that the descriptors BCUTp-1 h, JGI4, and maxwHBa were the key descriptors in the biodegradability effect by the sensitivity analysis of the QSAR model. Coupled with the results of PCDDs biodegradability 3D-QSAR model, BCUTp-1 h, JGI4, and maxwHBa were confirmed as the main descriptors that affect the biodegradability of dioxins. This study provides a novel theoretical perspective for the research of the biodegradation of both PCDDs and PCDFs dioxins.
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Affiliation(s)
- Qing Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Hao Yang
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Ning Hao
- College of New Energy and Environment, Jilin University, Changchun, 130012, China.
| | - Meijn Du
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Yuanyuan Zhao
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Xixi Li
- Center for Environmental Health Risk Assessment and Research, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3X5, Canada.
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16
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Hong D, Chang H, He X, Zhan Y, Tong R, Wu X, Li G. Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning. KIDNEY DISEASES (BASEL, SWITZERLAND) 2023; 9:433-442. [PMID: 37901708 PMCID: PMC10601920 DOI: 10.1159/000531619] [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: 10/20/2022] [Accepted: 06/05/2023] [Indexed: 10/31/2023]
Abstract
Introduction Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively. Conclusion Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.
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Affiliation(s)
- Daqing Hong
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Huan Chang
- Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin He
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ya Zhan
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Rongsheng Tong
- Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xingwei Wu
- Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Guisen Li
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
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17
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Dong J, Wang K, He J, Guo Q, Min H, Tang D, Zhang Z, Zhang C, Zheng F, Li Y, Xu H, Wang G, Luan S, Yin L, Zhang X, Dai Y. Machine learning-based intradialytic hypotension prediction of patients undergoing hemodialysis: A multicenter retrospective study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107698. [PMID: 37429246 DOI: 10.1016/j.cmpb.2023.107698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 05/22/2023] [Accepted: 06/24/2023] [Indexed: 07/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Intradialytic hypotension (IDH) is closely associated with adverse clinical outcomes in HD-patients. An IDH predictor model is important for IDH risk screening and clinical decision-making. In this study, we used Machine learning (ML) to develop IDH model for risk prediction in HD patients. METHODS 62,227 dialysis sessions were randomly partitioned into training data (70%), test data (20%), and validation data (10%). IDH-A model based on twenty-seven variables was constructed for risk prediction for the next HD treatment. IDH-B model based on ten variables from 64,870 dialysis sessions was developed for risk assessment before each HD treatment. Light Gradient Boosting Machine (LightGBM), Linear Discriminant Analysis, support vector machines, XGBoost, TabNet, and multilayer perceptron were used to develop the predictor model. RESULTS In IDH-A model, we identified the LightGBM method as the best-performing and interpretable model with C- statistics of 0.82 in Fall30Nadir90 definitions, which was higher than those obtained using the other models (P<0.01). In other IDH standards of Nadir90, Nadir100, Fall20, Fall30, and Fall20Nadir90, the LightGBM method had a performance with C- statistics ranged 0.77 to 0.89. As a complementary application, the LightGBM model in IDH-B model achieved C- statistics of 0.68 in Fall30Nadir90 definitions and 0.69 to 0.78 in the other five IDH standards, which were also higher than the other methods, respectively. CONCLUSION Use ML, we identified the LightGBM method as the good-performing and interpretable model. We identified the top variables as the high-risk factors for IDH incident in HD-patient. IDH-A and IDH-B model can usefully complement each other for risk prediction and further facilitate timely intervention through applied into different clinical setting.
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Affiliation(s)
- Jingjing Dong
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
| | - Kang Wang
- Department of Nephrology, the Second Affiliated Hospital of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Jingquan He
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Qi Guo
- Shenzhen Yuchen Medical Technology Co., Ltd. Co., Ltd, Shenzhen 518020, China
| | - Haodi Min
- Shenzhen Yuchen Medical Technology Co., Ltd. Co., Ltd, Shenzhen 518020, China
| | - Donge Tang
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Zeyu Zhang
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
| | - Cantong Zhang
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Fengping Zheng
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Yixi Li
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
| | - Huixuan Xu
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Gang Wang
- Department of Nephrology, University of Chinese Academy of Sciences Shenzhen Hospital (Guangming), Shenzhen 518020, China
| | - Shaodong Luan
- Departments of Nephrology, Shenzhen Longhua District Central Hospital, Shenzhen 518020, China
| | - Lianghong Yin
- Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China.
| | - Xinzhou Zhang
- Department of Nephrology, the Second Affiliated Hospital of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China.
| | - Yong Dai
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China.
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Lee H, Moon SJ, Kim SW, Min JW, Park HS, Yoon HE, Kim YS, Kim HW, Yang CW, Chung S, Koh ES, Chung BH. Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence model. Nephrol Dial Transplant 2023; 38:2310-2320. [PMID: 37019834 DOI: 10.1093/ndt/gfad064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) that is associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of this study was to develop a deep learning-based artificial intelligence (AI) model to predict IDH using pre-dialysis features. METHODS Data from 2007 patients with 943 220 HD sessions at seven university hospitals were used. The performance of the deep learning model was compared with three machine learning models (logistic regression, random forest and XGBoost). RESULTS IDH occurred in 5.39% of all studied HD sessions. A lower pre-dialysis blood pressure (BP), and a higher ultrafiltration (UF) target rate and interdialytic weight gain in IDH sessions compared with non-IDH sessions, and the occurrence of IDH in previous sessions was more frequent among IDH sessions compared with non-IDH sessions. Matthews correlation coefficient and macro-averaged F1 score were used to evaluate both positive and negative prediction performances. Both values were similar in logistic regression, random forest, XGBoost and deep learning models, developed with data from a single session. When combining data from the previous three sessions, the prediction performance of the deep learning model improved and became superior to that of other models. The common top-ranked features for IDH prediction were mean systolic BP (SBP) during the previous session, UF target rate, pre-dialysis SBP, and IDH experience during the previous session. CONCLUSIONS Our AI model predicts IDH accurately, suggesting it as a reliable tool for HD treatment.
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Affiliation(s)
- Hanbi Lee
- Transplantation Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | | | | | - Ji Won Min
- Department of Internal Medicine, Bucheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Hoon Suk Park
- Department of Internal Medicine, Eunpyeong St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hye Eun Yoon
- Department of Internal Medicine, Incheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Young Soo Kim
- Department of Internal Medicine, Uijeongbu St Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Hyung Wook Kim
- Department of Internal Medicine, St Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Chul Woo Yang
- Transplantation Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sungjin Chung
- Division of Nephrology, Department of Internal Medicine, Yeouido St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Eun Sil Koh
- Division of Nephrology, Department of Internal Medicine, Yeouido St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Byung Ha Chung
- Transplantation Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Halvorson BD, Bao Y, Ward AD, Goldman D, Frisbee JC. Regulation of Skeletal Muscle Resistance Arteriolar Tone: Integration of Multiple Mechanisms. J Vasc Res 2023; 60:245-272. [PMID: 37769627 DOI: 10.1159/000533316] [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: 01/20/2023] [Accepted: 07/27/2023] [Indexed: 10/03/2023] Open
Abstract
INTRODUCTION Physiological system complexity represents an imposing challenge to gaining insight into how arteriolar behavior emerges. Further, mechanistic complexity in arteriolar tone regulation requires that a systematic determination of how these processes interact to alter vascular diameter be undertaken. METHODS The present study evaluated the reactivity of ex vivo proximal and in situ distal resistance arterioles in skeletal muscle with challenges across the full range of multiple physiologically relevant stimuli and determined the stability of responses over progressive alterations to each other parameter. The five parameters chosen for examination were (1) metabolism (adenosine concentration), (2) adrenergic activation (norepinephrine concentration), (3) myogenic activation (intravascular pressure), (4) oxygen (superfusate PO2), and (5) wall shear rate (altered intraluminal flow). Vasomotor tone of both arteriole groups following challenge with individual parameters was determined; subsequently, responses were determined following all two- and three-parameter combinations to gain deeper insight into how stimuli integrate to change arteriolar tone. A hierarchical ranking of stimulus significance for establishing arteriolar tone was performed using mathematical and statistical analyses in conjunction with machine learning methods. RESULTS Results were consistent across methods and indicated that metabolic and adrenergic influences were most robust and stable across all conditions. While the other parameters individually impact arteriolar tone, their impact can be readily overridden by the two dominant contributors. CONCLUSION These data suggest that mechanisms regulating arteriolar tone are strongly affected by acute changes to the local environment and that ongoing investigation into how microvessels integrate stimuli regulating tone will provide a more thorough understanding of arteriolar behavior emergence across physiological and pathological states.
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Affiliation(s)
- Brayden D Halvorson
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
| | - Yuki Bao
- Department of Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
| | - Aaron D Ward
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Daniel Goldman
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
- Department of Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
| | - Jefferson C Frisbee
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
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Li Y, Yang H, He W, Li Y. Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis. Int J Mol Sci 2023; 24:13548. [PMID: 37686353 PMCID: PMC10488033 DOI: 10.3390/ijms241713548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/11/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.
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Affiliation(s)
| | | | | | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; (Y.L.); (H.Y.); (W.H.)
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Cai P, Lin Q, Lv D, Zhang J, Wang Y, Wang X. Establishment of a scoring model for the differential diagnosis of white coat hypertension and sustained hypertension. Blood Press Monit 2023; 28:185-192. [PMID: 37115849 PMCID: PMC10309104 DOI: 10.1097/mbp.0000000000000646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/19/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVES This study aimed to establish a scoring model for the differential diagnosis of white coat hypertension (WCH) and sustained hypertension (SHT). METHODS This study comprised 553 adults with elevated office blood pressure, normal renal function, and no antihypertensive medications. Through questionnaire investigation and biochemical detection, 17 parameters, such as gender and age, were acquired. WCH and SHT were distinguished by 24 h ambulatory blood pressure monitoring. The participants were randomly divided into a training set (445 cases) and a validation set (108 cases). The above parameters were screened using least absolute shrinkage and selection operator regression and univariate logistic regression analysis in the training set. Afterward, a scoring model was constructed through multivariate logistic regression analysis. RESULTS Finally, six parameters were selected, including isolated systolic hypertension, office systolic blood pressure, office diastolic blood pressure, triglyceride, serum creatinine, and cardiovascular and cerebrovascular diseases. Multivariate logistic regression was used to establish a scoring model. The R2 and area under the ROC curve (AUC) of the scoring model in the training set were 0.163 and 0.705, respectively. In the validation set, the R2 of the scoring model was 0.206, and AUC was 0.718. The calibration test results revealed that the scoring model had good stability in both the training and validation sets (mean square error = 0.001, mean absolute error = 0.014; mean square error = 0.001, mean absolute error = 0.025). CONCLUSION A stable scoring model for distinguishing WCH was established, which can assist clinicians in identifying WCH at the first diagnosis.
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Affiliation(s)
- Peng Cai
- Department of Cardiology, Institute of Field Surgery, Daping Hospital, Army Medical University, Chongqing
- Department of Intensive Care Medicine, PLA 80th Group Army Hospital, Weifang
| | - Qingshu Lin
- Department of Intensive Care Medicine, PLA 80th Group Army Hospital, Weifang
| | - Dan Lv
- Department of Intensive Care Medicine, PLA 80th Group Army Hospital, Weifang
| | - Jing Zhang
- Department of Intensive Care Medicine, PLA 80th Group Army Hospital, Weifang
| | - Yan Wang
- Department of Pharmacy, Key Laboratory of Basic Pharmacology of Ministry of Education Joint International Research Laboratory of Ministry Education, Zunyi Medical University, Zunyi
| | - Xukai Wang
- Department of Cardiology, Institute of Field Surgery, Daping Hospital, Army Medical University, Chongqing
- Department of Cardiology, Chongqing Hygeia Hospital, Chongqing, China
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22
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Inoue H, Oya M, Aizawa M, Wagatsuma K, Kamimae M, Kashiwagi Y, Ishii M, Wakabayashi H, Fujii T, Suzuki S, Hattori N, Tatsumoto N, Kawakami E, Asanuma K. Predicting dry weight change in Hemodialysis patients using machine learning. BMC Nephrol 2023; 24:196. [PMID: 37386392 PMCID: PMC10308746 DOI: 10.1186/s12882-023-03248-5] [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: 04/17/2023] [Accepted: 06/19/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient's physical conditions. METHODS All medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session. RESULTS The areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward. CONCLUSIONS The random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice.
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Affiliation(s)
- Hiroko Inoue
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Megumi Oya
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan
| | - Masashi Aizawa
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Kyogo Wagatsuma
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan
| | - Masatomo Kamimae
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan
| | - Yusuke Kashiwagi
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Masayoshi Ishii
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan
| | - Hanae Wakabayashi
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan
| | - Takayuki Fujii
- Department of Nephrology, Seirei Sakura Citizen hospital, Sakura, Chiba, Japan
| | - Satoshi Suzuki
- Department of Nephrology, Seirei Sakura Citizen hospital, Sakura, Chiba, Japan
| | - Noriyuki Hattori
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan
- Department of Emergency and Critical Care Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Narihito Tatsumoto
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan.
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan.
| | - Katsuhiko Asanuma
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan.
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Fan X, Wang J, Xia L, Qiu H, Tian Y, Zhangcai Y, Luo X, Gao Y, Li C, Wu Y, Zhao W, Chen J, Shi W, Yuan J, Ke S, Chen Y. Efficacy of endoscopic therapy for T1b esophageal cancer and construction of prognosis prediction model: a retrospective cohort study. Int J Surg 2023; 109:1708-1719. [PMID: 37132192 PMCID: PMC10389357 DOI: 10.1097/js9.0000000000000427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/21/2023] [Indexed: 05/04/2023]
Abstract
BACKGROUND The efficacy of endoscopic therapy on the long-term survival outcomes of T1b oesophageal cancer (EC) is unclear, this study was designed to clarify the survival outcomes of endoscopic therapy and to construct a model for predicting the prognosis in T1b EC patients. METHODS This study was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017 of patients with T1bN0M0 EC. Cancer-specific survival (CSS) and overall survival (OS) were compared between endoscopic therapy group, esophagectomy group and chemoradiotherapy group, respectively. Stabilized inverse probability treatment weighting was used as the main analysis method. The propensity score matching method and an independent dataset from our hospital were used as sensitivity analysis. The least absolute shrinkage and selection operator regression (Lasso) was employed to sift variables. A prognostic model was then established and was verified in two external validation cohorts. RESULTS The unadjusted 5-year CSS was 69.5% (95% CI, 61.5-77.5) for endoscopic therapy, 75.0% (95% CI, 71.5-78.5) for esophagectomy and 42.4% (95% CI, 31.0-53.8) for chemoradiotherapy. After stabilized inverse probability treatment weighting adjustment, CSS and OS were similar in endoscopic therapy and esophagectomy groups ( P =0.32, P =0.83), while the CSS and OS of chemoradiotherapy patients were inferior to endoscopic therapy patients ( P <0.01, P <0.01). Age, histology, grade, tumour size, and treatment were selected to build the prediction model. The area under the curve of receiver operating characteristics of 1, 3, and 5 years in the validation cohort 1 were 0.631, 0.618, 0.638, and 0.733, 0.683, 0.768 in the validation cohort 2. The calibration plots also demonstrated the consistency of predicted and actual values in the two external validation cohorts. CONCLUSION Endoscopic therapy achieved comparable long-term survival outcomes to esophagectomy for T1b EC patients. The prediction model developed performed well in calculating the OS of patients with T1b EC.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Jingping Yuan
- Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, P. R. China
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Spencer A, Samaan M, Noehren B. Monitoring Knee Contact Force with Force-Sensing Insoles. SENSORS (BASEL, SWITZERLAND) 2023; 23:4900. [PMID: 37430813 DOI: 10.3390/s23104900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/22/2023] [Accepted: 05/06/2023] [Indexed: 07/12/2023]
Abstract
Numerous applications exist for monitoring knee contact force (KCF) throughout activities of daily living. However, the ability to estimate these forces is restricted to a laboratory setting. The purposes of this study are to develop KCF metric estimation models and explore the feasibility of monitoring KCF metrics via surrogate measures derived from force-sensing insole data. Nine healthy subjects (3F, age 27 ± 5 years, mass 74.8 ± 11.8 kg, height 1.7 ± 0.08 m) walked at multiple speeds (0.8-1.6 m/s) on an instrumented treadmill. Thirteen insole force features were calculated as potential predictors of peak KCF and KCF impulse per step, estimated with musculoskeletal modeling. The error was calculated with median symmetric accuracy. Pearson product-moment correlation coefficients defined the relationship between variables. Models develop per-limb demonstrated lower prediction error than those developed per-subject (KCF impulse: 2.2% vs 3.4%; peak KCF: 3.50% vs. 6.5%, respectively). Many insole features are moderately to strongly associated with peak KCF, but not KCF impulse across the group. We present methods to directly estimate and monitor changes in KCF using instrumented insoles. Our results carry promising implications for internal tissue loads monitoring outside of a laboratory with wearable sensors.
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Affiliation(s)
- Alex Spencer
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, KY 40508, USA
- Department of Kinesiology & Health Promotion, College of Education, University of Kentucky, Lexington, KY 40508, USA
- Department of Biomedical Engineering, College of Engineering, University of Kentucky, Lexington, KY 40508, USA
| | - Michael Samaan
- Department of Kinesiology & Health Promotion, College of Education, University of Kentucky, Lexington, KY 40508, USA
- Department of Biomedical Engineering, College of Engineering, University of Kentucky, Lexington, KY 40508, USA
- Department of Orthopedic Surgery and Sports Medicine, College of Medicine, University of Kentucky, Lexington, KY 40508, USA
| | - Brian Noehren
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, KY 40508, USA
- Department of Orthopedic Surgery and Sports Medicine, College of Medicine, University of Kentucky, Lexington, KY 40508, USA
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Peimankar A, Winther TS, Ebrahimi A, Wiil UK. A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders. SENSORS (BASEL, SWITZERLAND) 2023; 23:679. [PMID: 36679471 PMCID: PMC9866459 DOI: 10.3390/s23020679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/25/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.
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Hsieh WH, Ku CCY, Hwang HPC, Tsai MJ, Chen ZZ. Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:375-383. [PMID: 37435541 PMCID: PMC10332468 DOI: 10.1109/jtehm.2023.3234207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/24/2022] [Accepted: 12/28/2022] [Indexed: 09/30/2023]
Abstract
Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement-With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverseevents.
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Affiliation(s)
- Wen-Huai Hsieh
- Department of SurgeryChang-Hua HospitalMinistry of Health and WelfareChanghua513007Taiwan
| | - Cooper Cheng-Yuan Ku
- Institute of Information Management, National Yang Ming Chiao Tung UniversityHsinchu300093Taiwan
| | - Humble Po-Ching Hwang
- Institute of Information Management, National Yang Ming Chiao Tung UniversityHsinchu300093Taiwan
| | - Min-Juei Tsai
- Department of NephrologyChang-Hua HospitalMinistry of Health and WelfareChanghua513007Taiwan
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Wang XZ, Wu HL, Wang T, Chen AQ, Sun HB, Ding ZW, Chang HY, Yu RQ. Rapid identification and semi-quantification of adulteration in walnut oil by using excitation–emission matrix fluorescence spectroscopy coupled with chemometrics and ensemble learning. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Wu Y, Jia M, Xiang C, Fang Y. Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective. BMC Geriatr 2022; 22:900. [PMID: 36434518 PMCID: PMC9700973 DOI: 10.1186/s12877-022-03576-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 11/01/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty. METHODS This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers' trust in the model. RESULTS Two distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors. CONCLUSIONS Interpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.
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Affiliation(s)
- Yafei Wu
- grid.12955.3a0000 0001 2264 7233School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen, 361102 Fujian China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian China
| | - Maoni Jia
- grid.12955.3a0000 0001 2264 7233School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen, 361102 Fujian China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian China
| | - Chaoyi Xiang
- grid.12955.3a0000 0001 2264 7233School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen, 361102 Fujian China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian China
| | - Ya Fang
- grid.12955.3a0000 0001 2264 7233School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen, 361102 Fujian China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian China
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Pasha Syed AR, Anbalagan R, Setlur AS, Karunakaran C, Shetty J, Kumar J, Niranjan V. Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers. BMC Bioinformatics 2022; 23:496. [DOI: 10.1186/s12859-022-05050-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/10/2022] [Indexed: 11/19/2022] Open
Abstract
AbstractClassification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.
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Khodabakhshi MB, Eslamyeh N, Sadredini SZ, Ghamari M. Cuffless blood pressure estimation using chaotic features of photoplethysmograms and parallel convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107131. [PMID: 36137326 DOI: 10.1016/j.cmpb.2022.107131] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/26/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE As a nonlinear framework in dynamical system analysis, chaotic approaches are mainly applied to evolve the complexity of biological systems. Due to the chaotic nature of the cardiovascular systems, the nonlinear features can intuitively provide a reliable framework in blood pressure (BP) estimation. Cuffless BP estimation is usually carried out by establishing deep neural network models estimating the BP values through machine-learned features of photoplethysmogram (PPG) signals. METHODS In this study, a novel parallel deep architecture is proposed to handle the machine-learned and chaotic features of PPG signals in estimating the actual BP values. The chaotic handcrafted features were the signal properties associated with the Poincare sections in the phase space and the recurrence plot-based measures called recurrence quantification analysis (RQA). Moreover, the measures quantifying the nonlinear properties of the temporal sequences such as correlation dimension, fractal dimension, Lyapunov exponent, and entropy-based quantities were also employed. The parallel architecture not only embedded the chaotic nature of PPG signals but also provided a facility to include the pseudo-periodic variations of PPGs by utilizing a concatenating layer. RESULTS Our framework was examined on the public dataset, namely, Multi-Parameter Intelligent in Intensive Care II contained the recording of PPG, ECG and arterial blood pressure. The performance of the employed handcrafted features in distinguishing between the levels of BP values was investigated based on Spearman's statistics. In addition, our proposed scheme is evaluated in terms of Pearson's correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The best performance was achieved when the employed handcrafted features accompanied by PPG sequences were applied to the parallel deep network. In particular, the values of R, RMSE, and MAE were obtained 0.9529, 2.76 mmHg, and 1.73 mmHg for diastolic BP, and 0.9444, 6.18 mmHg, and 3.8 mmHg for systolic BP, respectively. Moreover, based on the requirements of the standards set by the British Hypertension Society (BHS), the proposed scheme achieved a grade of A. CONCLUSIONS Our proposed scheme outperformed the state-of-the-art BP estimation methods. In addition, the results confirmed that the concatenation of the PPG-related machine-learned and nonlinear handcrafted features can be properly applied in continuous BP monitoring.
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Affiliation(s)
- Mohammad Bagher Khodabakhshi
- Department of Biomedical Engineering, Hamedan University of Technology, Mardom St, Shahid Fahmideh Blvd, Hamedan 6516913733, Iran.
| | - Naeem Eslamyeh
- Department of Biomedical Engineering, Hamedan University of Technology, Mardom St, Shahid Fahmideh Blvd, Hamedan 6516913733, Iran
| | - Seyede Zohreh Sadredini
- Department of Biomedical Engineering, Hamedan University of Technology, Mardom St, Shahid Fahmideh Blvd, Hamedan 6516913733, Iran
| | - Mohammad Ghamari
- Department of Computer Engineering, Hamedan University of Technology, Hamedan 6516913733, Iran
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Li Y, Zhao D, Liu G, Liu Y, Bano Y, Ibrohimov A, Chen H, Wu C, Chen X. Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine. Front Neuroinform 2022; 16:956423. [PMID: 36387587 PMCID: PMC9659657 DOI: 10.3389/fninf.2022.956423] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/28/2022] [Indexed: 09/19/2023] Open
Abstract
Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Guangjie Liu
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Yi Liu
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yasmeen Bano
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Alisherjon Ibrohimov
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Chengwen Wu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Xumin Chen
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou University, Wenzhou, China
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Fatahi R, Nasiri H, Homafar A, Khosravi R, Siavoshi H, Chehreh Chelgani S. Modeling operational cement rotary kiln variables with explainable artificial intelligence methods – a “conscious lab” development. PARTICULATE SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1080/02726351.2022.2135470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Rasoul Fatahi
- School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Nasiri
- Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Arman Homafar
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
| | - Rasoul Khosravi
- Department of Mining, Faculty of Engineering, Lorestan University, Khorramabad, Iran
| | - Hossein Siavoshi
- Department of Mining and Geological Engineering, University of Arizona, Tucson, USA
| | - Saeed Chehreh Chelgani
- Minerals and Metallurgical Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden
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Athaya T, Choi S. Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth. BIOSENSORS 2022; 12:bios12080655. [PMID: 36005051 PMCID: PMC9405546 DOI: 10.3390/bios12080655] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/10/2022] [Accepted: 08/16/2022] [Indexed: 12/01/2022]
Abstract
Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.
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Affiliation(s)
- Tasbiraha Athaya
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Sunwoong Choi
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
- Correspondence:
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Yun HR, Huh CW, Jung DH, Lee G, Son NH, Kim JH, Youn YH, Park JC, Shin SK, Lee SK, Lee YC. Machine Learning Improves the Prediction Rate of Non-Curative Resection of Endoscopic Submucosal Dissection in Patients with Early Gastric Cancer. Cancers (Basel) 2022; 14:cancers14153742. [PMID: 35954406 PMCID: PMC9367410 DOI: 10.3390/cancers14153742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/25/2022] [Accepted: 07/29/2022] [Indexed: 02/01/2023] Open
Abstract
Non-curative resection (NCR) of early gastric cancer (EGC) after endoscopic submucosal dissection (ESD) can increase the burden of additional treatment and medical expenses. We aimed to develop a machine-learning (ML)-based NCR prediction model for EGC prior to ESD. We obtained data from 4927 patients with EGC who underwent ESD between January 2006 and February 2020. Ten clinicopathological characteristics were selected using extreme gradient boosting (XGBoost) and were used to develop a ML-based model. Dataset was divided into the training and internal validation sets and verified using an external validation set. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were evaluated. The performance of each model was compared by using the Delong test. A total of 1100 (22.1%) patients were identified as being treated non-curatively with ESD. Seven ML-based NCR prediction models were developed. The performance of NCR prediction was highest in the XGBoost model (AUROC, 0.851; 95% confidence interval, 0.837–0.864). When we compared the prediction performance by the Delong test, XGBoost (p = 0.02) and support vector machine (p = 0.02) models showed a significantly higher performance among the NCR prediction models. We developed an ML model capable of accurately predicting the NCR of EGC before ESD. This ML model can provide useful information for decision-making regarding the appropriate treatment of EGC before ESD.
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Affiliation(s)
- Hae-Ryong Yun
- Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea;
| | - Cheal Wung Huh
- Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea;
- Correspondence: (C.W.H.); (D.H.J.)
| | - Da Hyun Jung
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.C.P.); (S.K.S.); (S.K.L.); (Y.C.L.)
- Correspondence: (C.W.H.); (D.H.J.)
| | - Gyubok Lee
- Graduate School of AI, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea;
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu 42601, Korea;
| | - Jie-Hyun Kim
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.-H.K.); (Y.H.Y.)
| | - Young Hoon Youn
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.-H.K.); (Y.H.Y.)
| | - Jun Chul Park
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.C.P.); (S.K.S.); (S.K.L.); (Y.C.L.)
| | - Sung Kwan Shin
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.C.P.); (S.K.S.); (S.K.L.); (Y.C.L.)
| | - Sang Kil Lee
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.C.P.); (S.K.S.); (S.K.L.); (Y.C.L.)
| | - Yong Chan Lee
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul 16995, Korea; (J.C.P.); (S.K.S.); (S.K.L.); (Y.C.L.)
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35
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Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition. Comput Biol Med 2022; 147:105752. [DOI: 10.1016/j.compbiomed.2022.105752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/22/2022]
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36
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Fatahi R, Nasiri H, Dadfar E, Chehreh Chelgani S. Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach. Sci Rep 2022; 12:7543. [PMID: 35534588 PMCID: PMC9085744 DOI: 10.1038/s41598-022-11429-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/25/2022] [Indexed: 11/30/2022] Open
Abstract
Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.
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37
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Malayeri AB, Khodabakhshi MB. Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals. Sci Rep 2022; 12:6633. [PMID: 35459260 PMCID: PMC9033848 DOI: 10.1038/s41598-022-10244-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/31/2022] [Indexed: 11/09/2022] Open
Abstract
Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless BP estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, multi-parameter intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05 ± 5.26 mmHg and 1.58 ± 2.6 mmHg, in turn.
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Affiliation(s)
- Ali Bahari Malayeri
- Department of Electrical Engineering, Shahid Beheshti University, 1983969411, Tehran, Iran
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Chen D, Liu J, Zang L, Xiao T, Zhang X, Li Z, Zhu H, Gao W, Yu X. Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients. Int J Biol Sci 2022; 18:360-373. [PMID: 34975338 PMCID: PMC8692161 DOI: 10.7150/ijbs.66913] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/12/2021] [Indexed: 12/12/2022] Open
Abstract
Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.
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Affiliation(s)
- Dongjie Chen
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China
| | - Jixing Liu
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.,Department of Nephrology, Institute of Nephrology, 2nd Affiliated Hospital of Hainan Medical University, Haikou, Hainan, P.R. China
| | - Longjun Zang
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China
| | - Tijun Xiao
- Department of General Surgery, Shaoyang University Affiliated Second Hospital, Shaoyang University, Shaoyang, Hunan, P.R. China
| | - Xianlin Zhang
- Department of General Surgery, Affiliated Renhe Hospital of China Three Gorges University, Yichang, Hubei, P.R. China
| | - Zheng Li
- Department of General Surgery, Affiliated Renhe Hospital of China Three Gorges University, Yichang, Hubei, P.R. China
| | - Hongwei Zhu
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China
| | - Wenzhe Gao
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China
| | - Xiao Yu
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China
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Xu J, Chen D, Deng X, Pan X, Chen Y, Zhuang X, Sun C. Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit. Int Wound J 2022; 19:1637-1649. [PMID: 35077000 PMCID: PMC9615270 DOI: 10.1111/iwj.13764] [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: 11/06/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 02/03/2023] Open
Abstract
The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large-scale clinical data. A retrospective cohort study design was employed to develop and validate a top-performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five-fold cross-validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23-point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 ± 0.07 and 0.84 ± 0.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.
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Affiliation(s)
- Jie Xu
- Department of Thoracic SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Danxiang Chen
- Department of Breast SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Xiaofang Deng
- Nursing departmentWenzhou Medical UniversityWenzhouChina
| | - Xiaoyun Pan
- Department of Thoracic SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Yu Chen
- Nursing DepartmentThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Xiaoming Zhuang
- Department of Thoracic SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Caixia Sun
- Nursing DepartmentThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
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Fernandes F, Stefenon SF, Seman LO, Nied A, Ferreira FCS, Subtil MCM, Klaar ACR, Leithardt VRQ. Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-212788] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.
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Affiliation(s)
- Filipe Fernandes
- Electrical Engineering Graduate Program, Santa Catarina State University. R. Paulo Malschitzki, North Industrial Zone, Joinville, Brazil
| | - Stéfano Frizzo Stefenon
- Electrical Engineering Graduate Program, Santa Catarina State University. R. Paulo Malschitzki, North Industrial Zone, Joinville, Brazil
- Fondazione Bruno Kessler, Istituto per la Ricerca Scientifica e Tecnologica. ViaSommarive, Povo, Trento, Italy
- Computer Scienceand Artificial Intelligence, University of Udine. Via delleScienze 206, 33100 Udine, Italy
| | - Laio Oriel Seman
- Graduate Programin Applied Computer Science, University of Vale do Itajaí. Uruguai 458, Centro, Itajaí, 88302-202, Brazil
| | - Ademir Nied
- Electrical Engineering Graduate Program, Santa Catarina State University. R. Paulo Malschitzki, North Industrial Zone, Joinville, Brazil
| | | | | | | | - Valderi Reis Quietinho Leithardt
- VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnicode Portalegre. 7300-555 Portalegre, Portugal
- COPELABS, Universidade Lusófona deHumanidades e Tecnologias. Campo Grande 376, 1749-024 Lisboa, Portugal
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Accuracy versus reliability-based modelling approaches for medical decision making. Comput Biol Med 2021; 141:105138. [PMID: 34929467 DOI: 10.1016/j.compbiomed.2021.105138] [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: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 11/21/2022]
Abstract
Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.
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Cui S, Qiu H, Wang S, Wang Y. Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis. J Clin Med 2021; 10:jcm10215021. [PMID: 34768540 PMCID: PMC8584535 DOI: 10.3390/jcm10215021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.
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Remaining Useful Life Prediction of the Concrete Piston Based on Probability Statistics and Data Driven. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper proposes a method on predicting the remaining useful life (RUL) of a concrete piston of a concrete pump truck based on probability statistics and data-driven approaches. Firstly, the average useful life of the concrete piston is determined by probability distribution fitting using actual life data. Secondly, according to condition monitoring data of the concrete pump truck, a concept of life coefficient of the concrete piston is proposed to represent the influence of the loading condition on the actual useful life of individual concrete pistons, and different regression models are established to predict the RUL of the concrete pistons. Finally, according to the prediction result of the concrete piston at different life stages, a replacement warning point is established to provide support for the inventory management and replacement plan of the concrete piston.
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Yun HR, Lee G, Jeon MJ, Kim HW, Joo YS, Kim H, Chang TI, Park JT, Han SH, Kang SW, Kim W, Yoo TH. Erythropoiesis stimulating agent recommendation model using recurrent neural networks for patient with kidney failure with replacement therapy. Comput Biol Med 2021; 137:104718. [PMID: 34481182 DOI: 10.1016/j.compbiomed.2021.104718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 12/17/2022]
Abstract
In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these patients is a challenging problem because of the complexities of the underlying diseases and heterogeneous responses to erythropoiesis-stimulating agents (ESAs). Therefore, we propose a ESA dose recommendation model based on sequential awareness neural networks. Data from 466 KFRT patients (12,907 dialysis sessions) in seven tertiary-care general hospitals were included in the experiment. First, a Hb prediction model was developed to simulate longitudinal heterogeneous ESA and Hb interactions. Based on the prediction model as a prospective study simulator, we built an ESA dose recommendation model to predict the required amount of ESA dose to reach a target hemoglobin level after 30 days. Each model's performance was evaluated in the mean absolute error (MAE). The MAEs presenting the best results of the prediction and recommendation model were 0.59 (95% confidence interval: 0.56-0.62) g/dL and 43.2 μg (ESAs dose), respectively. Compared to the results in the real-world clinical data, the recommendation model achieved a reduction of ESA dose (Algorithm: 140 vs. Human: 150 μg/month, P < 0.001), a more stable monthly Hb difference (Algorithm: 0.6 vs. Human: 0.8 g/dL, P < 0.001), and an improved target Hb success rate (Algorithm: 79.5% vs. Human: 62.9% for previous month's Hb < 10.0 g/dL; Algorithm: 95.7% vs. Human:73.0% for previous month's Hb 10.0-12.0 g/dL). We developed an ESA dose recommendation model for optimizing anemia management in patients with KFRT and showed its potential effectiveness in a simulated prospective study.
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Affiliation(s)
- Hae-Ryong Yun
- Division of Nephrology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Gyubok Lee
- Graduate School of AI, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Myeong Jun Jeon
- Intelligence Laboratories, Nexon Korea, Seongnam, South Korea
| | - Hyung Woo Kim
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
| | - Young Su Joo
- Division of Nephrology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyoungnae Kim
- Division of Nephrology, Soonchunhyang University Hospital, Seoul, South Korea
| | - Tae Ik Chang
- Department of Internal Medicine, National Health Insurance Service Medical Center, Ilsan Hospital, Goyang, Gyeonggi-do, South Korea
| | - Jung Tak Park
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
| | - Seung Hyeok Han
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea; Department of Internal Medicine, College of Medicine, Severance Biomedical Science Institute, Brain Korea 21 PLUS, Yonsei University, Seoul, South Korea
| | - Wooju Kim
- Department of Industrial Engineering, College of Engineering, Yonsei University, Seoul, South Korea.
| | - Tae-Hyun Yoo
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea.
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Lee Y, Ryu J, Kang MW, Seo KH, Kim J, Suh J, Kim YC, Kim DK, Oh KH, Joo KW, Kim YS, Jeong CW, Lee SC, Kwak C, Kim S, Han SS. Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma. Sci Rep 2021; 11:15704. [PMID: 34344909 PMCID: PMC8333365 DOI: 10.1038/s41598-021-95019-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 07/20/2021] [Indexed: 12/17/2022] Open
Abstract
The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783-0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.
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Affiliation(s)
- Yeonhee Lee
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea.,Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu-si, Gyeonggi-do, South Korea
| | - Jiwon Ryu
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea
| | - Min Woo Kang
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea
| | - Kyung Ha Seo
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, South Korea
| | - Jayoun Kim
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, South Korea
| | - Jungyo Suh
- Department of Urology, Seoul National University College of Medicine, Seoul, South Korea
| | - Yong Chul Kim
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea
| | - Kook-Hwan Oh
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea
| | - Kwon Wook Joo
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea
| | - Yon Su Kim
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea
| | - Chang Wook Jeong
- Department of Urology, Seoul National University College of Medicine, Seoul, South Korea
| | - Sang Chul Lee
- Department of Urology, Seoul National University College of Medicine, Seoul, South Korea
| | - Cheol Kwak
- Department of Urology, Seoul National University College of Medicine, Seoul, South Korea.
| | - Sejoong Kim
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea. .,Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea. .,Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea.
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea.
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Yang Z, Yang F, Yang M, Qi Y, Jiang M, Xuan J, Liu Y, Tao H, Liu Y, Wang F. Prediction of overall survival in patients with Stage I esophageal cancer: A novel web-based calculator. J Surg Oncol 2021; 124:767-779. [PMID: 34263466 DOI: 10.1002/jso.26594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/27/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND AIMS In this study, we aimed to develop a convenient web-based calculator to predict the overall survival (OS) of patients with Stage I esophageal cancer (EC). METHODS Data of 1664 patients, between 2004 and 2015, were extracted from the Surveillance, Epidemiology, and End Results database. Least absolute shrinkage and selection operator regression was employed to sift variables; subsequently, Cox proportional hazards regression model was built. We applied the enhanced bootstrap validation to appraise the discrimination and calibration of the model. Clinical benefit was measured using decision curve analysis (DCA). Thereafter, a web-based calculator based on the model, which could be used to predict the 1-, 3-, and 5-year OS rates, was developed. RESULTS Race, age, histologic type, grade, N stage, and therapeutic methods were selected. C-indices of the prediction model in the training and validation groups were 0.726 (95% confidence interval [CI], 0.679-0.773) and 0.724 (95% CI, 0.679-0.769), respectively. Calibration curves showed good agreement between the groups. The DCA demonstrated that the prediction model is clinically useful. CONCLUSIONS The prediction model we developed showed a good performance in calculating the OS rates in patients with Stage I EC. The web-based calculator is available at https://championship.shinyapps.io/dynnomapp/.
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Affiliation(s)
- Zhuoxin Yang
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Fengwu Yang
- Department of Laboratory Medicine, Shandong Guoxin Healthcare Group Zibo Hospital, Zibo, China
| | - Miaofang Yang
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ying Qi
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Mingzuo Jiang
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ji Xuan
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yu Liu
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hui Tao
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yuxiu Liu
- Data and Statistics Unit of Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China.,Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Fangyu Wang
- Department of Gastroenterology and Hepatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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Waterlogging Resistance Evaluation Index and Photosynthesis Characteristics Selection: Using Machine Learning Methods to Judge Poplar’s Waterlogging Resistance. MATHEMATICS 2021. [DOI: 10.3390/math9131542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Flood disasters are the major natural disaster that affects the growth of agriculture and forestry crops. Due to rapid growth and strong waterlogging resistance characteristics, many studies have explained the waterlogging resistance mechanism of poplar from different perspectives. However, there is no accurate method to define the evaluation index of waterlogging resistance. In addition, there is also a lack of research on predicting the waterlogging resistance of poplars. Based on the changes of poplar biomass and seedling height, the evaluation index of poplar resistance to waterlogging was well determined, and the characteristics of photosynthesis were used to predict the waterlogging resistance of poplars. First, four methods of hierarchical clustering, lasso, stepwise regression and all-subsets regression were used to extract the photosynthesis characteristics. After that, the support vector regression model of poplar resistance to waterlogging was established by using the characteristic parameters of photosynthesis. Finally, the results show that the SVR model based on Stepwise regression and Lasso method has high precision. On the test set, the coefficient of determination (R2) was 0.8581 and 0.8492, the mean square error (MSE) was 0.0104 and 0.0341, and the mean relative error (MRE) was 9.78% and 9.85%, respectively. Therefore, using the characteristic parameters of photosynthesis to predict the waterlogging resistance of poplars is feasible.
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