Should a propensity score model be super? The utility of ensemble procedures for causal adjustment.
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in medicine"
DOI: 10.1002/sim.8075
Abstract: In investigations of the effect of treatment on outcome, the propensity score is a tool to eliminate imbalance in the distribution of confounding variables between treatment groups. Recent work has suggested that Super Learner, an… read more here.
Keywords: regression; treatment; propensity score; super learner ... See more keywords
Stacked generalization: an introduction to super learning
Sign Up to like & getrecommendations! Published in 2018 at "European Journal of Epidemiology"
DOI: 10.1007/s10654-018-0390-z
Abstract: Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into a host of methods… read more here.
Keywords: generalization; stacked generalization; introduction super; super learner ... See more keywords
A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-019-51338-y
Abstract: In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment.… read more here.
Keywords: motion; tumor motion; super learner; learner model ... See more keywords
G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes
Sign Up to like & getrecommendations! Published in 2021 at "Scientific Reports"
DOI: 10.1038/s41598-021-81110-0
Abstract: In clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used… read more here.
Keywords: machine; machine learning; computation; causal effects ... See more keywords
Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study.
Sign Up to like & getrecommendations! Published in 2023 at "American journal of epidemiology"
DOI: 10.1093/aje/kwad113
Abstract: Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, but fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of… read more here.
Keywords: influenza hospitalizations; prediction; simulation study; ensemble super ... See more keywords
Super LeArner Prediction of NAb Panels (SLAPNAP): A Contain-erized Tool for Predicting Combination Monoclonal Broadly Neu-tralizing Antibody Sensitivity.
Sign Up to like & getrecommendations! Published in 2021 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btab398
Abstract: MOTIVATION A single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g., cocktails, multi-specific antibodies), which demonstrate… read more here.
Keywords: combination; learner prediction; nab panels; monoclonal broadly ... See more keywords
Practical considerations for specifying a super learner.
Sign Up to like & getrecommendations! Published in 2022 at "International journal of epidemiology"
DOI: 10.1093/ije/dyad023
Abstract: Common tasks encountered in epidemiology, including disease incidence estimation and causal inference, rely on predictive modelling. Constructing a predictive model can be thought of as learning a prediction function (a function that takes as input… read more here.
Keywords: considerations specifying; prediction; practical considerations; epidemiology ... See more keywords
Personal Credit Default Discrimination Model Based on Super Learner Ensemble
Sign Up to like & getrecommendations! Published in 2021 at "Mathematical Problems in Engineering"
DOI: 10.1155/2021/5586120
Abstract: 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… read more here.
Keywords: credit; default; base classifier; model ... See more keywords
Occupations on the map: Using a super learner algorithm to downscale labor statistics
Sign Up to like & getrecommendations! Published in 2022 at "PLOS ONE"
DOI: 10.1371/journal.pone.0278120
Abstract: Detailed and accurate labor statistics are fundamental to support social policies that aim to improve the match between labor supply and demand, and support the creation of jobs. Despite overwhelming evidence that labor activities are… read more here.
Keywords: learner algorithm; labor; labor statistics; occupation ... See more keywords