Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.

The prevalence of risk for hearing impairment in newborns with congenital syphilis in a newborn hearing screening program (NHS)

Objective

To study the prevalence of risk for hearing impairment in neonates with congenital syphilis in a newborn hearing screening program.

Study design

The study design is retrospective, documentary, and is cross-sectional. The sample consisted of newborns who were born between January 2019 and December 2021 and who underwent neonatal hearing screening in a public maternity hospital. Demographic data and the presence and specification of risk indicators for hearing impairment (RIHL) were collected. In retest cases, the results and the final score were also collected. For data analysis, the Kruskal-Wallis and Conover-Iman post-hoc tests were used, comparing the groups that passed and failed the hearing screening that had RIHL, using a significance level of p of <0.5.

Results

Among the RIHL observed in the sample, prematurity was more frequent in newborns who passed the screening (55.26%) than in those who failed the test (45.67%). Congenital syphilis was the ninth most frequent RIHL (8.04%) among the newborns who passed the test and the 15th factor (3.03%), with the highest occurrence in those who failed the hearing screening. When comparing the two groups (pass and fail), we found significant differences (p < 0.05) between them.

Conclusion

Congenital syphilis was the ninth risk indicator for the most common hearing impairment and, in isolation, did not present a risk for failure in neonatal hearing screening. Notably, congenital syphilis can cause late hearing loss during child development. Thus, there is an indication of audiological monitoring of these neonates.