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.

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.

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter.

Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images.

Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images.

Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis of the disease due to its low cost and that it does not emit harmful radiation, and it gives good results when applied in young women.

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.