Decision tree model development and in silico validation for avoidable hospital readmissions at 30 days in a pediatric population.

Background and objective: identifying patients at high risk of avoidable readmission remains a challenge for healthcare professionals. Despite the recent interest in Machine Learning in this topic, studies are scarce and commonly using only black box algorithms. The aim of our study was to develop and validate in silico an interpretable predictive model using a decision tree inference to identify pediatric patients at risk of 30-day potentially avoidable readmissions.

Machine learning for hospital readmission prediction in pediatric population.

Background And Objective: Pediatric readmissions are a burden on patients, families, and the healthcare system. In order to identify patients at higher readmission risk, more accurate techniques, as machine learning (ML), could be a good strategy to expand the knowledge in this area. The aim of this study was to develop predictive models capable of identifying children and adolescents at high risk of potentially avoidable 30-day readmission using ML.

Measuring water holding capacity in pork meat images using deep learning.

Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method.

Validation of the HOSPITAL score as predictor of 30-day potentially avoidable readmissions in pediatric hospitalized population: retrospective cohort study.

Potentially avoidable pediatric readmissions are a burden to patients and their families. Identifying patients with higher risk of readmission could help minimize hospital costs and facilitate the targeting of care interventions. HOSPITAL score is a tool developed and widely used to predict adult patient's readmissions; however its predictive capacity for pediatric readmissions has not yet been evaluated.

An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.

Coronavirus Disease-2019 (COVID-19) causes Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and has opened several challenges for research concerning diagnosis and treatment. Chest X-rays and computed tomography (CT) scans are effective and fast alternatives to detect and assess the damage that COVID causes to the lungs at different stages of the disease. Although the CT scan is an accurate exam, the chest X-ray is still helpful due to the cheaper, faster, lower radiation exposure, and is available in low-incoming countries.

A new index for quantifying the ornamentational complexity of animals with shells.

Morphological complexity reflects the biological structure of an organism and is closely linked to its associated functions and phylogenetics. In animals with shells, ornamentation is an important characteristic of morphological complexity, and it has various functions. However, because of the variations in type, shape, density, and strength of ornamentation, a universal quantitative measure of morphological complexity for shelled animals is lacking.

Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialists diagnose this disease. Microscopy image analysis is the most economical method for the initial screening of patients with ALL, but this task is subjective and time-consuming.

Automatic Segmentation of Bone Canals in Histological Images.

The literature provides many works that focused on cell nuclei segmentation in histological images. However, automatic segmentation of bone canals is still a less explored field. In this sense, this paper presents a method for automatic segmentation approach to assist specialists in the analysis of the bone vascular network.

A study of publication trajectories of the Brazilian Computer Science community.

The average faculty productivity have been described as a rapid rise-short peak-gradual decline pattern. Way et al. (2017) have studied this pattern for faculty careers in Computer Science in North America using a piecewise linear model.

Fibrous Dysplasia Characterization Using Lacunarity Analysis.

Fibrous dysplasia (FD) is a developmental anomaly in which the normal medullary space of the affected bone is replaced by fibro-osseous tissue. This condition is typically encountered in adolescents and young adults. It affects the maxillofacial region and it can often cause severe deformity and asymmetry.

Medical image retrieval and analysis by Markov random fields and multi-scale fractal dimension.

Many Content-based Image Retrieval (CBIR) systems and image analysis tools employ color, shape and texture (in a combined fashion or not) as attributes, or signatures, to retrieve images from databases or to perform image analysis in general. Among these attributes, texture has turned out to be the most relevant, as it allows the identification of a larger number of images of a different nature. This paper introduces a novel signature which can be used for image analysis and retrieval.

Color texture classification using shortest paths in graphs.

Color textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze color textures by modeling a color image as a graph in two different and complementary manners (each color channel separately and the three color channels altogether) and by obtaining statistical moments from the shortest paths between specific vertices of this graph. Such an approach allows to create a set of feature vectors, which were extracted from VisTex, USPTex, and TC00013 color texture databases.

Shape analysis using fractal dimension: a curvature based approach.

The present work shows a novel fractal dimension method for shape analysis. The proposed technique extracts descriptors from a shape by applying a multi-scale approach to the calculus of the fractal dimension. The fractal dimension is estimated by applying the curvature scale-space technique to the original shape.