Prediction-based variable selection for component-wise gradient boosting.

Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms for the variable selection step in model-based component-wise gradient boosting.

MELD-score for risk stratification in cardiac surgery.

The outcome of the patients undergoing cardiac surgery with cardiopulmonary bypass (CPB) is also influenced by the renal and hepatic organ functions. Risk stratification, using scores such as EURO Score II or STS Short-Term Risk Calculator for patients undergoing cardiac surgery with cardiopulmonary bypass, ignores the quantitative renal and hepatic function; therefore, MELD-Score was applied in these cases. We retrospectively examined patient data using the MELD score as a predictor of mortality.

Bayesian learners in gradient boosting for linear mixed models.

Selection of relevant fixed and random effects without prior choices made from possibly insufficient theory is important in mixed models. Inference with current boosting techniques suffers from biased estimates of random effects and the inflexibility of random effects selection. This paper proposes a new inference method "BayesBoost" that integrates a Bayesian learner into gradient boosting with simultaneous estimation and selection of fixed and random effects in linear mixed models.

Gradient boosting for linear mixed models.

Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand.

Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques.

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.

Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques.

Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting and likelihood-based boosting. An extensive framework has been proposed in order to fit generalized mixed models based on boosting, however for the case of cluster-constant covariates likelihood-based boosting approaches tend to mischoose variables in the selection step leading to wrong estimates. We propose an improved boosting algorithm for linear mixed models, where the random effects are properly weighted, disentangled from the fixed effects updating scheme and corrected for correlations with cluster-constant covariates in order to improve quality of estimates and in addition reduce the computational effort.

Development of a new sternal dehiscence prediction scale for decision making in sternal closure techniques after cardiac surgery.

Background: After sternotomy, the spectrum for sternal osteosynthesis comprises standard wiring and more complex techniques, like titanium plating. The aim of this study is to develop a predictive risk score that evaluates the risk of sternum instability individually. The surgeon may then choose an appropriate sternal osteosynthesis technique that is risk- adjusted as well as cost-effective.

Safety of medical compression stockings in patients with diabetes mellitus or peripheral arterial disease.

Introduction: Compression therapy is highly effective in the treatment of many venous diseases, including leg edema. However, its relevance in patients with peripheral arterial disease (PAD) or diabetes mellitus is critically discussed. The aim of the present study was to assess the influence of compression therapy on microperfusion and its safety in patients with PAD or diabetes mellitus.

Influence of end-stage renal disease on long-term survival after major amputation.

: To analyze long-term outcomes and possible influencing factors in patients with endstage renal disease (ESRD) and critical limb ischemia (CLI) after major amputation compared to patients with normal renal function and non-dialysis-dependent chronic kidney disease. : Abstraction of single-center medical records of patients undergoing above knee (AKA) and below knee (BKA) amputation over a 10 years period (n = 436; 2009-2018). Excluded were amputations due to trauma or tumor.