Utilization of machine learning for dengue case screening.

Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment.

Analysis of notified drug poisoning among children in Santa Catarina state, 2016-2020.

Objective: The aim of this study was to analyze the incidence of drug poisoning in children registered in the Santa Catarina Information and Toxicological Assistance Center between 2016 and 2020.

Methods: This observational epidemiological study, with a historical cohort design, was carried out from reported cases of drug poisoning in children aged 0-12 years. Census sampling was used to collect data.

Validation of a computerized decision support system to review pharmacotherapy treatment: scheduling guidelines.

Background: The review of pharmacotherapy can be conceptualized as a service in which the drugs used by the patient are reviewed to control the risks as well as to improve the results of the drug therapy, detecting, solving, and preventing issues associated with the drug, readjusting the doses and times (schedule) so that the treatment is not incompatible or in duplicity.

Methods: The aim of the study was to validate an intelligent information system, which was developed to assist the scheduling activity in the pharmacotherapy review. The system used the concept of Genetic Algorithms.

PROFILE AND APPROPRIATE USE OF ANTIBIOTICS AMONG CHILDREN IN A GENERAL HOSPITAL IN SOUTHERN BRAZIL.

Objective: To examine the profile and appropriate use of antibiotics among hospitalized children.

Methods: A cross-sectional study was conducted with children who had taken antibiotics during hospitalization in a private philanthropic hospital in Southern Brazil, from January to December 2015. The data were obtained by reviewing medical records, encompassing demographic data (age, gender, ethnicity, and body weight) and clinical data (causes of hospitalization, use of antibiotics, and clinical outcome).

Structured Pyramidal Neural Networks.

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN).

[Evaluation of the quality of propranolol and enalapril tablets supplied in the public health system in a southern Brazilian city].

The acquisition of medication by the Brazilian public health service through bidding processes based on the lowest price criterion is a source of concern with respect to the quality of the products offered to the population. The scope of this work was to evaluate the quality of Enalapril 10 mg and Propranolol 40 mg tablets bought via the bidding process and supplied by the public health system in a city in the state of Santa Catarina, Brasil, over the course of a year. The visual aspect, weight variation, friability, drug content and dissolving time were analyzed.

Preanesthesia evaluation and reduction of preoperative care costs.

Background And Objectives: Preanesthesia evaluation (PAE) is fundamental in the preparation of a surgical patient. Among its advantages is the reduction of preoperative care costs. Although prior studies had observed this benefit, it is not clear whether it can be taken into consideration among us.