Stacked Generalization: An Introduction to Super Learning

Naimi, Ashley I.; Balzer, Laura B.

doi:10.1101/172395

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…Because of this variability in performance, which by itself provides an assessment of the different ML algorithms, we then combined them into the SLA. The more sophisticated SLA demonstrated superiority over the other singularly used ML algorithms; the SLA has also been shown in other analyses to be the optimal tool for constructing such predictive models [27,28]. Such an approach may additionally be more efficient when the number of covariates is large for assessing the multiple covariate interactions and correlation terms than simpler statistical approaches.…”

Section: Discussionmentioning

confidence: 97%

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

Chow

Sakthivel³

et al. 2021

Adv Ther

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Introduction Administrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for body mass index (BMI), limits the sample sizes that can be analyzed to address certain research questions. The objective of this study was to construct models by implementing machine-learning (ML) algorithms to predict BMI classifications (≥ 30, ≥ 35, and ≥ 40 kg/m 2 ) in administrative healthcare claims databases, and then internally and externally validate them. Methods Five advanced ML algorithms were implemented for each BMI classification on a random sampling of BMI readings from the Optum PanTher Electronic Health Record database (2%) and the Optum Clinformatics Date of Death (20%) database, while incorporating baseline demographic and clinical characteristics. Sensitivity analyses with oversampling ratios were conducted. Model performance was validated internally and externally. Results Models trained on the Super Learner ML algorithm (SLA) yielded the best BMI classification predictive performance. SLA model 1 utilized sociodemographic and clinical characteristics, including baseline BMI values; the area under the receiver operating characteristic curve (ROC AUC) was approximately 88% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m 2 (internal validation), while accuracy ranged from 87.9% to 92.8% and specificity ranged from 91.8% to 94.7%. SLA model 2 utilized sociodemographic information and clinical characteristics, excluding baseline BMI values; ROC AUC was approximately 73% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m 2 (internal validation), while accuracy ranged from 73.6% to 80.0% and specificity ranged from 71.6% to 85.9%. The external validation on the MarketScan Commercial Claims and Encounters database yielded relatively consistent results with slightly diminished performance. Conclusion This study demonstrated the feasibility and validity of using ML algorithms to predict BMI classifications in administrative healthcare claims data to expand the utility for RWE generation. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-020-01605-6.

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Section: Discussionmentioning

confidence: 97%

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

Chow

Sakthivel³

et al. 2021

Adv Ther

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“…For each outcome, 20 imputations were performed and TMLE was performed on all 20 imputed datasets; estimates were then pooled across models. TMLE was performed using a cross-validated ensemble machine learning procedure (the Super Learner) 31 that fits the best-weighted combination of different models; candidate models included generalized linear models, generalized additive models, and multivariate adaptive regression splines. Models adjusted for patient gender, age at the baseline visit, baseline hearing ability, and annual follow-up visit audiometric outcomes.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Effects of Hearing Aids on Hearing Ability in Patients with Sensorineural Hearing Loss

et al. 2021

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Background A frequent concern surrounding amplification with hearing aids for patients with sensorineural hearing loss is whether these devices negatively affect hearing ability. To date, there have been few studies examining the long-term effects of amplification on audiometric outcomes in adults. Purpose In the present study, we examined how hearing aids affect standard audiometric outcomes over long-term periods of follow-up. Research Design We retrospectively collected audiometric data in adults with sensorineural hearing loss, constructing a model of long-term outcomes. Study Sample This retrospective cohort study included 802 ears from 401 adult patients with bilateral sensorineural hearing loss eligible for amplification with hearing aids at a single institution. Intervention Of the eligible patients, 88 were aided bilaterally, and 313 were unaided. Data Collection and Analysis We examined the standard three-frequency pure-tone average (PTA3-Freq), a novel extended pure-tone average (PTAExt), and word recognition score (WRS) per-ear at each encounter. We then modeled the association between the use of hearing aids for 5 years and these audiometric outcomes using targeted maximum likelihood estimation. Results In comparing aided and unaided ears at the end of 5 years, there were discernible effects for all measurements. The PTA3-Freq was 5 dB greater in aided ears (95% CI: 1.37–8.64, p = 0.007), WRS was 4.5 percentage points lower in aided ears (95% CI: −9.14 to 0.15, p = 0.058), and PTAExt was 5 dB greater in aided ears (95% CI: 2.18–7.82, p < 0.001), adjusting for measured confounders. Conclusion Our analysis revealed discernible effects of 5 years of hearing aid use on hearing ability, specifically as measured by the PTA3-Freq, novel PTAExt, and WRS, suggesting a greater decline in hearing ability in patients using hearing aids. Future studies are needed to examine these effects between treatment groups over longer periods of time and in more heterogeneous populations to improve clinical practice guidelines and safety of both prescriptive fitting nonprescriptive amplification.

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“…e Super Learner algorithm is an evolution of the Stacking algorithm. Previous research results indicate that the Super Learner integrated model proposed by Van der Laan et al can independently select the base classifier according to the data structure of the dataset and the performance of the classifier, so as to improve the classification performance and robustness of the model [38][39][40][41][42][43][44]. Besides, if none of the candidate models in the Super Learner's classifier algorithm library can achieve the prespecified accuracy, the performance of the Super Learner is at least as good as the best algorithm in the candidate algorithm library, or it gradually approaches the best algorithm.…”

Section: Heterogeneous Ensemblementioning

confidence: 99%

“…is paper uses the Z-score standardization method to standardize the data [39,41]. e Z-score standardization method is shown in…”

Section: Data Preprocessingmentioning

confidence: 99%

Personal Credit Default Discrimination Model Based on Super Learner Ensemble

Shen

et al. 2021

Mathematical Problems in Engineering

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Assessing the default of customers is an essential basis for personal credit issuance. This paper considers developing a personal credit default discrimination model based on Super Learner heterogeneous ensemble to improve the accuracy and robustness of default discrimination. First, we select six kinds of single classifiers such as logistic regression, SVM, and three kinds of homogeneous ensemble classifiers such as random forest to build a base classifier candidate library for Super Learner. Then, we use the ten-fold cross-validation method to exercise the base classifier to improve the base classifier’s robustness. We compute the base classifier’s total loss using the difference between the predicted and actual values and establish a base classifier-weighted optimization model to solve for the optimal weight of the base classifier, which minimizes the weighted total loss of all base classifiers. Thus, we obtain the heterogeneous ensembled Super Learner classifier. Finally, we use three real credit datasets in the UCI database regarding Australia, Japanese, and German and the large credit dataset GMSC published by Kaggle platform to test the ensembled Super Learner model’s effectiveness. We also employ four commonly used evaluation indicators, the accuracy rate, type I error rate, type II error rate, and AUC. Compared with the base classifier’s classification results and heterogeneous models such as Stacking and Bstacking, the results show that the ensembled Super Learner model has higher discrimination accuracy and robustness.

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Stacked Generalization: An Introduction to Super Learning

Cited by 16 publications

References 27 publications

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

Long-Term Effects of Hearing Aids on Hearing Ability in Patients with Sensorineural Hearing Loss

Personal Credit Default Discrimination Model Based on Super Learner Ensemble

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