A Scalable and Automated Machine Learning Framework to Support Risk Management

Ferreira, Luís; Pilastri, André; Martins, Carlos Manuel; Santos, Pedro; Cortez, Paulo

doi:10.1007/978-3-030-71158-0_14

Cited by 8 publications

(10 citation statements)

References 17 publications

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“…With the rapid changes in novel treatment patterns, demographics, and patient populations, data shifts have been increasingly recognized and have significantly affected predictive performance over time [ 63 , 64 ]. The rapid adjustment of autoML predictive performance with new data is more feasible than non-automated ML models [ 40 ], and can improve time-efficient workflow in the model maintenance phase.…”

Section: Discussionmentioning

confidence: 99%

“…ML algorithms can handle nonlinear, complex, and multidimensional data [ 36 , 37 ], and recent studies have shown high predictive performance from ML algorithms that outperform traditional statistical analyses [ 38 , 39 ]. Recently, automated ML (autoML) has emerged as a growing field to minimize human input and effort on repetitive tasks in ML pipelines, such as optimal algorithm selection and hyperparameter optimization to achieve optimal performance [ 40 ], by replacing manual trial-and-error approaches with systematic data-driven decision making [ 41 , 42 ]. In addition, autoML uses automation to efficiently identify the algorithms or models that work best for each dataset and improves accuracy using the ensemble method of algorithms [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, autoML has been shown to be very effective, with high predictive performance comparable to human hyperparameter optimization (identification of hyperparameters that returns an optimal model) with a more time-efficient workflow and less human assistance [ 41 , 43 ]. In the present era of utilizing electronic health records (EHRs), where additional data is continuously added and updated, rapid adjustment of the scoring systems in autoML real-world applications is more feasible than traditional ML approaches [ 40 ]. Despite the growing research in the field of autoML there has been little work applying autoML to the healthcare field, despite demonstrated need [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Pattharanitima

Kattah

Mao

et al. 2022

JCM

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Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. Results: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. Conclusion: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Pattharanitima

Kattah

Mao

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…Also, H 2 O AutoML has both Python and web user interface, which facilitates the model training and practical implementation. 36 2.5. Evaluation of the Conventional and AutoML Algorithms.…”

Section: Machine Learning Model 241 Conventional Machine Learning Modelmentioning

confidence: 99%

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

Cheng,

Xu,

et al. 2023

ACS EST Eng.

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Accurate prediction of methane production in anaerobic digestion with various pretreatment strategies is of the utmost importance for efficient sludge treatment and resource recovery. Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. This work proposed a novel integrated system that employed an ensemble semisupervised learning (SSL)-automated ML (AutoML) model with limited variable inputs to reveal the effects of different pretreatments on methane production during sludge digestion with explainable analysis. Considering the direct correlations of the pretreatment type and digestion substrates, the pretreatment type is considered as a hidden variable. Results demonstrated that the AutoML model outperformed the conventional ML models (i.e., support vector regression (SVR), extreme gradient boosting (XGB), etc.), as evidenced by its higher R 2 value. Moreover, the integration of SSL further enhanced the prediction accuracy by effectively leveraging unlabeled data, leading to a reduction in the mean squared error from 11.3 to 9.7. Explainable analysis results revealed the significance of different variables and the utmost importance of operating time, followed by proteins, carbohydrates, chemical oxygen demand, and volatile fatty acids. Furthermore, principal component and correlation analysis unveiled the interconnected relationships among substrate concentration, microbial communities, and metabolic functions for methane production and found that the increasing substrate concentration promoted the enrichment of functional microbial and metabolic functions. These insights shed light on the advantages of SSL-AutoML in predicting methane production in anaerobic digestion systems and elucidate the dependence relationships with key variables, offering valuable guidance for effective sludge pretreatment with enhanced resource recovery capabilities.

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“…24,26 The PPMC coefficient was conducted by the Python code (see in the Supporting Materials). 21,27 Our previous study also proved the high performance of H 2 O AutoML. 22 Moreover, H 2 O AutoML has been proven to achieve high prediction performance in diverse fields.…”

Section: Data Source and Preprocessingmentioning

confidence: 99%

Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

Luo

et al. 2022

ACS EST Eng.

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The indirect carbon emission from electrical consumption of wastewater treatment plants (WWTPs) accounts for large proportions of their total carbon emissions, which deserves intensive attention. This work proposed an automated machine learning (AutoML)-based indirect carbon emission analysis (ACIA) approach and predicted the specific indirect carbon emission from electrical consumption (SE e ; kg CO 2 /m 3 ) successfully in nine fullscale WWTPs (W1−W9) with different treatment configurations based on the historical operational data. The stacked ensemble models generated by the AutoML accurately predicted the SE e (mean absolute error = 0.02232−0.02352, R 2 = 0.65107−0.67509). Then, the variable importance and Shapley additive explanations (SHAP) summary plots qualitatively revealed that the influent volume and the types of secondary and tertiary treatment processes were the most important variables associated with SE e prediction. The interpretation results of partial dependence and individual conditional expectation further verified quantitative relationships between input variables and SE e . Also, low energy efficiency with high indirect carbon emission of WWTPs was distinguished. Compared with traditional carbon emission analysis and prediction methods, the ACIA method could accurately evaluate and predict SE e of WWTPs with different treatment scales and processes with easily available variables and reveal qualitative and quantitative relationships inside datasets simultaneously, which is a powerful tool to benefit the "carbon neutrality" of WWTPs.

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A Scalable and Automated Machine Learning Framework to Support Risk Management

Cited by 8 publications

References 17 publications

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

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