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robust Length of Stay Prediction model for Indoor Patients

Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Auto machine learning-based modelling and prediction of excavationinduced tunnel displacement

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1100-1114页

作者： Dongmei Zhang Yiming Shen Zhongkai Huang Xiaochuang Xie Key Laboratory of Geotechnical and Underground Engineering Ministry of EducationTongji UniversityShanghai200092China Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai200092China

The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling ***,there lacks robust methods to predict excavation-induced tunnel *** this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the *** input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep *** 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s *** genetic algorithm(GA)-ML models are established as well for *** results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and *** analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the ***,the AutoML model is further validated by practical *** prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.

关键词： Soilestructure interaction Auto machine learning(AutoML) Displacement prediction robust model Geotechnical engineering

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Application of robust Strategies in Location Selection of Logistics Distribution Center for Fresh Agricultural Products

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Asian Agricultural Research 2020年第10期12卷 7-9页

作者： Liu YANG Bing ZHAO Pinyuan ZHAO Bingqing ZHANG Xuejie BAI Hebei Agricultural University Baoding 071001China

In view of the uncertainty in the location selection of logistics distribution center for the fresh agricultural products,the present study established a robust model based on the maximization of principal component score taking budget cost parameters as an *** the process of model solving,the interval form of the uncertain set was used to clarify the constraint conditions,to transform into a certain 0-1 integer linear programming model,so as to solve with the aid of LINGO ***,through studying the location selection of logistics distribution center for fresh agricultural products in the Beijing-Tianjin-Hebei region,it analyzed the application of the robust model and tested the validity of the model.

关键词： Fresh agricultural products Logistics distribution Center location robust model

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