SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine

Guo, Hua; Wang, Jikui; Ao, Wei; He, Yulin

doi:10.1155/2018/4058403

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…Previous ML studies of biomarkers in PE have -in very small samples -obtained AUCs of 0.90 -0.96, including many more markers [51][52][53]. In this study we have not, except for maternal weight, included maternal risk factors [15], so it must be expected that a clinical performance will be better.…”

Section: Discussionmentioning

confidence: 92%

“…The reason for this difference is explained by the ability for the QLattice model to capture the Gaussian interaction between Re and Lp. Previous ML studies of biomarkers in PE have – in very small samples – obtained AUCs of 0.90 – 0.96, including many more markers [51-53]. In this study we have not, except for maternal weight, included maternal risk factors [15], so it must be expected that a clinical performance will be better.…”

Section: Discussionmentioning

confidence: 95%

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Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia

Wilstrup

Hedley

Rode

et al. 2022

Preprint

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Objectives: Preeclampsia (PE) is an important cause of perinatal morbidity and mortality. Despite an elusive pathophysiology, PE has been associated with changes in maternal serum concentrations of adipokines in early pregnancy. We hypothesized, that symbolic regression might identify interactions between adipokines and PE, which may have eluded regression and Bayesian models. Methods: In this nested case–control sub–study, of the Copenhagen First Trimester Screening Study, data regarding maternal weight and serum concentrations of PAPP–A, leptin (Lp), soluble leptin receptor (sLR), adiponectin, and resistin (Re) was available from 423 first trimester pregnancies (gestational week 10+3–13+6), 126 of which developed PE. Symbolic regression with QLattice/Feyn 2.1 was used to identify models comprising two–interactions between up-to three markers. Results: The optimal mathematical model exhibited a non–linear relation between Re and the combined effect of sLR and Lp. The model was dependent, in a Gaussian way, on the level of Re. The receiver operating characteristic (ROC) curve of the model viz. identification of PE cases in first trimester had an AUC of 0.81 and a modelled DR of 40 % for a FPR of 4 %. Symbolic regression outperformed logistic regression of the same parameters with a ROC with AUC = 0.77, and a DR of 7 % for a 3 % FPR. Conclusions: Symbolic regression identified non-linear interactions between Lp, sLR and Re concentrations in first trimester pregnancy serum of pregnancies which later developed PE. Non-linear interactions suggest new pathophysiological pathways and may help in designing more efficient screening protocols for PE.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 95%

Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia

Wilstrup

Hedley

Rode

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Extreme Gradient Boosting (XGBoost) and random forest (RF) algorithms were used to predict the probability of septic arthritis. The XGBoost algorithm is an extendible gradient boosting machine that can be applied to both regression and classification [ 6 ]. Since its introduction in 2014, the impact of XGBoost has been widely recognised in several machine learning and data-mining challenges.…”

Section: Methodsmentioning

confidence: 99%

Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

Choi

Sim

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application (www.septicknee.com). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).

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“…For prediction of sarcopenia, the binary classification (sarcopenia/normal) model of XG Boost was used. The XGBoost model is one of the most commonly used machine learning models for solving both regression and classification problems and has been widely adopted to classify and predict medical events [3,45,46]. The synthetic minority oversampling technique (SMOTE) was utilised to overcome the potential bias arising from class imbalances, by creating synthetic minority class samples (the sarcopenia group in this case) [47,48].…”

Section: Machine Learning Model For Sarcopenia Predictionmentioning

confidence: 99%

Deep Learning-Based Muscle Segmentation and Quantification of Full-Leg Plain Radiograph for Sarcopenia Screening in Patients Undergoing Total Knee Arthroplasty

Hwang

Ahn

Park

et al. 2022

JCM

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Sarcopenia, an age-related loss of skeletal muscle mass and function, is correlated with adverse outcomes after some surgeries. Here, we present a deep-learning-based model for automatic muscle segmentation and quantification of full-leg plain radiographs. We illustrated the potential of the model to predict sarcopenia in patients undergoing total knee arthroplasty (TKA). A U-Net-based deep learning model for automatic muscle segmentation was developed, trained and validated on the plain radiographs of 227 healthy volunteers. The radiographs of 403 patients scheduled for primary TKA were reviewed to test the developed model and explore its potential to predict sarcopenia. The proposed deep learning model achieved mean IoU values of 0.959 (95% CI 0.959–0.960) and 0.926 (95% CI 0.920–0.931) in the training set and test set, respectively. The fivefold AUC value of the sarcopenia classification model was 0.988 (95% CI 0.986–0.989). Of seven key predictors included in the model, the predicted muscle volume (PMV) was the most important of these features in the decision process. In the preoperative clinical setting, wherein laboratory tests and radiographic imaging are available, the proposed deep-learning-based model can be used to screen for sarcopenia in patients with knee osteoarthritis undergoing TKA with high sarcopenia screening performance.

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SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine

Cited by 8 publications

References 25 publications

Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia

Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia

Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

Deep Learning-Based Muscle Segmentation and Quantification of Full-Leg Plain Radiograph for Sarcopenia Screening in Patients Undergoing Total Knee Arthroplasty

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