Computer methods in biomechanics and biomedical engineering
Jan 31, 2018
Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions ...
OBJECTIVE: Free-breathing abdomen imaging requires non-rigid motion registration of unavoidable respiratory motion in three-dimensional undersampled data sets. In this work, we introduce an image registration method based on the convolutional neural ...
Most of the tools and diagnosis models of Masticatory Efficiency (ME) are not well documented or severely limited to simple image processing approaches. This study presents a novel expert system for ME assessment based on automatic recognition of mix...
BACKGROUND: Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and ris...
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally ...
Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded during an oddball task would predict cognitive functioning and information processing speed in Mult...
The depression, anxiety and physiosomatic symptoms (DAPS) of schizophrenia are associated with negative symptoms and changes in tryptophan catabolite (TRYCAT) patterning. The aim of this study is to delineate the associations between DAPS and psychos...
BACKGROUND: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and health administrators. The current study aimed at developing and comparing some statistical models to i...
OBJECTIVE: We investigated the potential of computer-based models to decode diagnosis and lifetime consumption in alcohol dependence (AD) from grey-matter pattern information. As machine-learning approaches to psychiatric neuroimaging have recently c...
OBJECTIVES: We validate a machine learning-based sepsis-prediction algorithm () for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specifi...
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