Bridge deck deterioration modeling is critical to infrastructure management. Deterioration traditionally done using deterministic models, stochastic and recently basic machine learning methods. The advanced learning-based survival such as random forest, have not been adapted for use in This paper introduces forest models bridge compare their performance with a commonly used traditional model, t...

BACKGROUND Death is uncommon in thyroid cancer patients, and the factors important in predicting survival remain inadequately studied. The objective of this study was to assess prognostic effects of patient, tumor, and treatment factors and to determine prognostic groups for thyroid cancer survival. METHODS Using data from the Surveillance, Epidemiology, and End Results Program (SEER), we eva...

The perception of prosodic prominence is influenced by different sources like different acoustic cues, linguistic expectations and context. We use a generalized additive model and a random forest to model the perceived prominence on a corpus of spoken German. Both models are able to explain over 80% of the variance. While the random forests give us some insights on the relative importance of th...

Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the generative mechanism of a graph. Yet given the complexity and inherent noise in real datasets, it is still very challenging to identify the best model for a given observed graph. We discuss a framework for graph model sele...

Background: Gastric cancer is the fifth most common cancer and the third top cause of cancer related death with about 1 million new cases and 700,000 deaths in 2012. The aim of this investigation was to identify important factors for outcome using a random survival forest (RSF) approach. Materials and Methods: Data were collected from 128 gastric cancer patients through a historical cohort stud...

BACKGROUND Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients' risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare 'Model-Base...