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ensemble machine learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis

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JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2024年 17卷 1589-1602页

作者： Wang, Tzu-Hao Kao, Chih-Chin Chang, Tzu-Hao Taipei Med Univ Shuang Ho Hosp Dept Med Educ Div Gen Med New Taipei City Taiwan Taipei Med Univ Coll Med Sci & Technol Grad Inst Biomed Informat Taipei Taiwan Taipei Med Univ Coll Med Sch Med Dept Internal MedDiv Nephrol Taipei Taiwan Taipei Med Univ Hosp Dept Internal Med Div Nephrol Taipei Taiwan Taipei Med Univ Taipei Med Univ Res Ctr Urol & Kidney TMU RCUK Taipei Taiwan Taipei Med Univ Hosp Clin Big Data Res Ctr Taipei Taiwan Taipei Med Univ Grad Inst Biomed Informat Coll Med Sci & Technol Int Ctr Hlth Informat Technol 9FEduc & Res BldgShuang Ho Campus301 Yuantong R New Taipei 235603 Taiwan

Purpose: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes. Patients and Methods: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform. Results: The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor. Conclusion: ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical dec

关键词： AKI-D dialysis prognosis ensemble machine learning prediction models risk factors

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ensemble machine learning models for predicting concrete compressive strength incorporating various sand types

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第4期8卷 1-35页

作者： Tipu, Rupesh Kumar Bansal, Shweta Batra, Vandna Patel, Gaurang A. KR Mangalam Univ Sch Engn & Technol Sohna 122103 Haryana India KR Mangalam Univ Ctr Excellence AI Sohna 122103 Haryana India Charotar Univ Sci & Technol CHARUSAT Chandubhai S Patel Inst Technol CSPIT Dept Mech Engn Changa 388421 Gujarat India

Accurate prediction of concrete compressive strength is essential in construction and material science. Traditional laboratory methods for measuring compressive strength rely on destructive testing, which can be expensive and slow. These limitations create hurdles in scenarios requiring frequent strength evaluations. This study explores the effectiveness of ensemble machine learning models for predicting concrete compressive strength, combining predictions from multiple base models: Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regressor (GBR), XGBoost, and Multi-layer Perceptron (MLP). Using a dataset of 587 samples that covers a wide range of mix compositions and sand types, the ensemble model achieved superior predictive accuracy over individual models. Specifically, the ensemble model attained an R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>2$$\end{document} score of 0.9876, Mean Squared Error (MSE) of 2.334, Mean Absolute Error (MAE) of 1.137 MPa, and Mean Absolute Percentage Error (MAPE) of 0.050, surpassing the top-performing base models (GBR and XGBoost). A SHAP (SHapley Additive exPlanations) analysis offered insight into feature importance, indicating that AGE, SP (Superplasticizer), and Binder Kg/m3 are key factors. These findings align with established knowledge about how curing age and binder content affect concrete strength. To encourage practical use of the trained model, a user-friendly Graphical User Interface (GUI) was developed, allowing real-time predictions for different mix designs. Hosted on GitHub, this interface can assist engineers and researchers in optimizing concrete mixes. This study demonstrates that an ensemble learning approach can offer accurate, interpretable, and accessible solutions for compres

关键词： Concrete compressive strength ensemble machine learning Feature importance Prediction models Sand types

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ensemble machine learning-based models for estimating the transfer length of strands in PSC beams

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期221卷

作者： Tran, Viet-Linh Kim, Jin-Kook Seoul Natl Univ Sci & Technol Dept Civil Engn 232 Gongneung Ro Seoul 01811 South Korea Vinh Univ Dept Civil Engn Vinh 461010 Vietnam

This study aims to develop four ensemble machine learning (ML) models, including Random Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), for estimating the transfer length of strands in prestressed concrete (PSC) beams. The results of eleven well-known empirical equations and four single ML models, including Support Vector machine (SVM), Multi-Layer Perceptron (MLP), K-nearest Neighbors (KNN), and Decision Tree (DT), are used to evaluate the performance of the developed ensemble ML models. This study shows that the GB and XGB models agree well with experimental results and significantly outperform empirical equations and single ML models. The SHapley Additive exPlanations method based on the GB and XGB models determines the compressive strength of concrete at prestress release, initial prestress, strand diameter, concrete cover, beam section width, and beam section height have the most significant effect on the transfer length of strands in PSC beams. Eventually, a web application is built based on the best ML models for practical use. It can predict the transfer length of strands in PSC beams without costly and timeconsuming tests.

关键词： ensemble machine learning Prestressed concrete beam Transfer length Web application

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ensemble machine learning methods in screening electronic health records: A scoping review

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DIGITAL HEALTH 2023年 9卷

作者： Stevens, Christophe A. T. Lyons, Alexander R. M. Dharmayat, Kanika, I Mahani, Alireza Ray, Kausik K. Vallejo-Vaz, Antonio J. Sharabiani, Mansour T. A. Imperial Coll London Imperial Ctr Cardiovasc Dis Prevent ICCP Sch Publ Hlth Dept Primary Care & Publ Hlth London England Davidson Kempner Capital Management Quantitat Res New York NY USA Univ Seville Fac Med Dept Med Seville Spain Univ Seville IBiS Hosp Univ Virgen Del Rocio Clin Epidemiol & Vasc Risk Inst Biomed Sevilla IBiS Seville Spain Imperial Coll London Sch Publ Hlth Dept Primary Care & Publ Hlth London England Imperial Coll London Imperial Ctr Cardiovasc Dis Prevent Dept Primary Care & Publ Hlth Charing Cross CampusReynolds BldgSt Dunstans Rd London W6 8RP England

BackgroundElectronic health records provide the opportunity to identify undiagnosed individuals likely to have a given disease using machine learning techniques, and who could then benefit from more medical screening and case finding, reducing the number needed to screen with convenience and healthcare cost savings. ensemble machine learning models combining multiple prediction estimates into one are often said to provide better predictive performances than non-ensemble models. Yet, to our knowledge, no literature review summarises the use and performances of different types of ensemble machine learning models in the context of medical pre-screening. MethodWe aimed to conduct a scoping review of the literature reporting the derivation of ensemble machine learning models for screening of electronic health records. We searched EMBASE and MEDLINE databases across all years applying a formal search strategy using terms related to medical screening, electronic health records and machine learning. Data were collected, analysed, and reported in accordance with the PRISMA scoping review guideline. ResultsA total of 3355 articles were retrieved, of which 145 articles met our inclusion criteria and were included in this study. ensemble machine learning models were increasingly employed across several medical specialties and often outperformed non-ensemble approaches. ensemble machine learning models with complex combination strategies and heterogeneous classifiers often outperformed other types of ensemble machine learning models but were also less used. ensemble machine learning models methodologies, processing steps and data sources were often not clearly described. ConclusionsOur work highlights the importance of deriving and comparing the performances of different types of ensemble machine learning models when screening electronic health records and underscores the need for more comprehensive reporting of machine learning methodologies employed in clinical research.

关键词： ensemble machine learning supervised machine learning mass screening electronic health records scoping review

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Water quality parameters-based prediction of dissolved oxygen in estuaries using advanced explainable ensemble machine learning

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 380卷 125146页

作者： Chen, Xingda Zhao, Chenyao Chen, Jinyue Jiang, Hao Li, Dan Zhang, Jing Han, Bo Chen, Shuisen Wang, Chongyang Chinese Acad Sci Guangzhou Inst Geochem Guangzhou 510640 Peoples R China Guangdong Acad Sci GuangDong Engn Technol Res Ctr Remote Sensing Big Key Lab Guangdong Utilizat Remote Sensing & Geog I Guangzhou Inst GeogGuangdong Open Lab Geospatial Guangzhou 510070 Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519082 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The dissolved oxygen (DO) is crucial for the ecological health of estuaries and bays. However, human activities, land-sea interactions, and the unclear impact mechanisms of water quality parameters (WQPs) pose challenges to DO prediction. Water quality models and statistical methods were used to achieve predictions previously, with gaps of low accuracy and unclear impact mechanism between WQPs and DO. Here, we present an interpretable ensemble machine learning (EML) framework for DO prediction and reveal that the impact mechanism of WQPs on DO variation of six estuaries in China. The results show: 1) DO have significant short-term fluctuations and a decreasing trend in most rivers from November 2020 to December 2023. Bagging-boosting model (BBM) have best performance in most rivers, while stacking model (SM) achieves better prediction in Jilong River (R2 is 0.71 and RMSE is 0.55), due to its capability of utilizing more WQP information (lag features of Electrical conductivity (EC), permanganate concentration (CODMn), pH and total nitrogen (TN)) to predict DO under larger variation condition. 2) 1-3 days lag features of DO and water temperature (WT) play a crucial role in DO prediction and the 1-day lagged DO makes the largest contribution (mean absolute SHapley Additive explanation (SHAP) value higher than 0.9). The lag features of pH have a positive impact on DO in most rivers. 3) The factors that makes largest influence on DO in the same day differ across different rivers. The impact of 1-day lagged DO on model prediction has a threshold around 10 mg/L and the interaction between WT and DO on the same day shows spatial heterogeneity across different rivers. EC, pH, and TN have a positive impact on the DO of the same day, while WT, ammonia nitrogen (NH3-N) and total phosphorus (TP) have a negative impact. 4) Errors in model prediction stem from two aspects, one is insufficient driving force of features when they correctly guide predictions toward true value. Anothe

关键词： Dissolved oxygen ensemble machine learning Interpretability Mechanism

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ensemble machine learning-Based Egg Parasitism Identification for Endangered Bird Conservation 15th

Ensemble Machine Learning-Based Egg Parasitism Identificatio...

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15th International Conference on Computational Collective Intelligence (ICCCI)

作者： Nhidi, Wiem Ben Aoun, Najib Ejbali, Ridha Univ Gabes Natl Engn Sch Gabes ENIG RTIM Res Team Intelligent Machines Gabes 6029 Tunisia Al Baha Univ Coll Comp Sci & Informat Technol Al Baha 657797738 Saudi Arabia Univ Sfax Natl Sch Engineers Sfax ENIS REGIM Lab Res Grp Intelligent Machines Sfax 3038 Tunisia

ISBN: (纸本)9783031417733;9783031417740

The phenomenon of intraspecific nest parasitism is of a great interest to biologists because it helps in the conservation of critically endangered birds like the Slender-billed Gull. Indeed, upon detecting a parasitic egg in the nest, the Slender-Billed Gull female abandon it. This behavior results in loosing large number of future birds and leads to its extension. So, a nest cleaning from parasitic eggs has been an urgent need. Therefore, in this paper, we suggest a Slender-Billed parasitic egg identification system from the egg visual information to clean the nest and avoid the female leaving. Encouraged by the success of the machine learning models in pattern recognition and classification, we have built a system which extracts the most important egg visual features and classify them using a set of machine learning models which have been aggregated together to attain a high parasitic egg identification accuracy. In fact, a set of egg visual features have been extracted from the eggshell using the SpotEgg tool which have provided information about the egg coloration and patterning such as the egg shape, size, color and spottiness. Our model has been evaluated on a 31-nest dataset and has given an accuracy of 88.3% which has outperformed other machine learning models.

关键词： ensemble machine learning Stacked Model SpotEgg Voting classifier Slender-Billed Gull Parasitic Egg Identification

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ensemble machine learning Models for Root Note Detection in Irish Instrumental Dance Music 31

Ensemble Machine Learning Models for Root Note Detection in ...

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31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)

作者： Shahid, Abdul Diamond, Danny McDermott, James d'Aquin, Mathieu Natl Coll Ireland Dublin Ireland Univ Galway Galway Ireland Univ Lorraine LORIA CNRS INRIA Nancy France

ISBN: (纸本)9798350360219

The tonal centre of a piece of music is crucial for perception and understanding, estimating the root note is an important issue in music information retrieval. To determine the root note of a melody, previously published methods generally involve matching the histogram of pitch classes in the melody with pre-calculated pitch class profiles. Performance varies between methods depending on these profiles, as there is no universally agreed-upon set of profiles. Furthermore, these methods do not consider structural information. In this study, we present an ensemble machine learning-based algorithm for root note prediction. In the proposed methodology, we estimate the key of a piece using several existing pitch-class profile algorithms and several simple key-estimation heuristics. We then feed these results into a machine-learning model. Experiments are conducted on the Ceol Rince na hEireann dataset, which contains 1,224 Irish traditional dance music melodies. The dataset is unbalanced, with some possible root notes occurring rarely or never, reflecting common practice in the tradition. To achieve a balanced dataset, we employed both simple oversampling and the Synthetic Minority Over-sampling Technique (SMOTE). The experiments revealed that the Random Forest classifier, trained on a balanced dataset, performed the best. The F1 score was approximately 0.90 when tested on unseen data.

关键词： Root note key detection ensemble machine learning SMOTE Irish traditional music

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An ensemble machine learning Approach for High-Resolution Estimation of Groundwater Storage Anomalies

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WATER 2025年第10期17卷 1445-1445页

作者： Yuan, Yanbin Shen, Dongyang Cao, Yang Wang, Xiang Zhang, Bo Dong, Heng Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China Ningbo Ecol & Environm Monitoring Ctr Zhejiang Pro Ningbo 315048 Peoples R China Zhejiang Spatiotemporal Sophon Bigdata Co Ltd Ningbo 315101 Peoples R China

Groundwater depletion has emerged as a pressing global challenge, yet the low spatial resolution (0.25 degrees) of Gravity Recovery and Climate Experiment (GRACE) satellite data limits its application in regional groundwater monitoring. In this study, based on 0.25 degrees spatial resolution groundwater storage anomalies (GWSAs) data derived from GRACE satellite observations and GLDAS hydrological model outputs, supplemented with hydrological data, humanities data, and other geographic parameters, we constructed a Stacking-based ensemble machine learning model that achieved a 1 km spatial resolution of GWSAs distribution data across the contiguous United States (CONUS) from 2010 to 2020. The ensemble model integrates eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LightGBM), and Categorical Boosting (CatBoost) models using an Attention-Based Dynamic Weight Allocation (ADWA) approach, along with a ridge regression model. The results indicate that our ensemble model outperforms individual machine learning (ML) models, achieving a coefficient of determination (R2) of 0.929, root mean square error (RMSE) of 25.232 mm, mean absolute error (MAE) of 19.125 mm, and Nash-Sutcliffe efficiency (NSE) of 0.936, validated by 10-fold cross-validation. In situ measurements indicate that, compared with the original data, approximately 61.7% of the monitoring wells (266 out of 431) exhibit a higher correlation after downscaling, with the overall correlation coefficient increasing by about 18.7%, which suggests that the downscaled product exhibits an appreciable improvement in accuracy. The ensemble model proposed in this study, by integrating the advantages of various ML algorithms, is better able to address the complexity and uncertainty of groundwater storage variations, thus providing scientific support for the sustainable management of groundwater resources.

关键词： groundwater depletion GRACE groundwater storage anomalies ensemble machine learning high resolution

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An investigation on ensemble machine learning algorithms for nonlinear stability response of a two-dimensional FG nanobeam

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第9期46卷 1-24页

作者： Tariq, Aiman Uzun, Busra Deliktas, Babur Yayli, Mustafa Ozgur Bursa Uludag Univ Engn Fac Dept Civil Engn Gorukle Campus TR-16059 Bursa Turkiye

In this paper, the nonlinear buckling analysis of two-dimensional functionally graded nanobeams is investigated using ensemble machine learning (ML) techniques and semi-analytical approach based on Fourier series and Stokes' transformation. ensemble models such as XG boosting, gradient boosting, light gradient boosting, adaptive boosting, random forest, and extra trees regressor are utilized to explore the complex relationship between different input features and the buckling loads of the nanobeams. The training data for these models are derived from the nonlinear strain gradient theory. Performance of ML models are evaluated using multiple metrics such as R2, MAE, MAPE, MSE and RMSE and visual representation techniques like Taylor plots, scatter plots, and box plots. Model interpretation using SHAP analysis is also employed for studying the impact and significance of each input feature on buckling loads. Among all the established models, light gradient boosting demonstrated superior performance in predicting the buckling loads accurately. It is shown that the ensemble ML models can accurately estimate the buckling loads of a two-dimensional functionally graded nanobeam with R2 value of 0.999 given the adequate amount of training data.

关键词： ensemble machine learning SHAP Fourier series Nonlinear buckling analysis Functionally graded nanobeam

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An explainable AI-enabled granular ensemble machine learning framework to demystify fertilizer price movements

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第8期75卷 1569-1586页

作者： Jana, Rabin K. Ghosh, Indranil Kumar, P. N. Ram Indian Inst Management Raipur Operat & Quantitat Methods Area Raipur Chhattisgarh India Inst Management Technol Hyderabad IT & Analyt Area Hyderabad Telangana India Indian Inst Management Kozhikode Quantitat Methods & Operat Management Area Kozhikode Kerala India

This paper proposes a novel explainable artificial intelligence (AI) driven ensemble machine learning (ML) framework for predicting fertilizer price movements and assessing the contributions of the technical and macroeconomic indicators. We integrate the Boruta algorithm, Maximal Overlap Discrete Wavelet Transformation (MODWT), Random Forest (RF), and explainable AI. The predictive analytics exercise utilises the residual of the previous stage as an additional indicator for arriving at the subsequent stage forecasts. We observe a significant influence of the residual in providing forecasts for time series with higher frequencies. The explainable AI is used at the global and local levels to explain the impacts of the indicators on fertilizer price movements. We have used monthly urea and diammonium phosphate (DAP) prices for nearly the last 30 years for predictive analytics. The explainable AI identifies the more significant impacts of the technical indicators compared to macroeconomic counterparts in forecasting urea and DAP prices at the global level. Also, the price movements of urea and DAP are similar at the global level. On the contrary, macroeconomic indicators influence more at the local level. The CBOE volatility index for urea, geopolitical risk, and commodity industrial input for DAP significantly influence the price movements at the local level.

关键词： Forecasting explainable AI ensemble machine learning fertilizer price time series

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