Abstract
Objectives: To develop and validate a clinical model to identify patients admitted to hospital with community-acquired infection (CAI) caused by pathogens resistant to antimicrobials recommended in current CAI treatment guidelines. Methods: International prospective cohort study of consecutive patients admitted with bacterial infection. Logistic regression was used to associate risk factors with infection by a resistant organism. The final model was validated in an independent cohort. Results: There were 527 patients in the derivation and 89 in the validation cohort. Independent risk factors identified were: atherosclerosis with functional impairment (Karnofsky index <70) [adjusted OR (aOR) (95% CI) = 2.19 (1.41-3.40)]; previous invasive procedures [adjusted OR (95% CI) = 1.98 (1.28-3.05)]; previous colonization with an MDR organism (MDRO) [aOR (95% CI) = 2.67 (1.48-4.81)]; and previous antimicrobial therapy [aOR (95% CI) = 2.81 (1.81-4.38)]. The area under the receiver operating characteristics (AU-ROC) curve (95% CI) for the final model was 0.75 (0.70-0.79). For a predicted probability >= 22% the sensitivity of the model was 82%, with a negative predictive value of 85%. In the validation cohort the sensitivity of the model was 96%. Using this model, unnecessary broad-spectrum therapy would be recommended in 30% of cases whereas undertreatment would occur in only 6% of cases. Conclusions: For patients hospitalized with CAI and none of the following risk factors: atherosclerosis with functional impairment; previous invasive procedures; antimicrobial therapy; or MDRO colonization, CAI guidelines can safely be applied. Whereas, for those with some of these risk factors, particularly if more than one, alternative antimicrobial regimens should be considered.

Abstract
Introduction: In 2015 the Directorate-General for Health of Portugal published new standards (DGS 001/2015) for the registration of cesarean section indicators. The existing scenario was the lack of data, influencing the quality of indicators and analyses on them. The use of a single computer tool was encouraged to register and compare indicators between hospitals with special attention to the Robson Classification as it employs basic information of pregnancy to classify all deliveries in 10 groups. The selected tool was Obscare software. Aim: Describe the scenario on data quality by analyzing the completeness of obstetric records from 2016 to 2018 of the variables used in Robson's classification collected by the Obscare tool. Methods: The completeness is evaluated using a number of missing values. The lower the completeness, the higher the number of missing values. Also, we perform the imputation of data based on basic concepts and analyzed the participation of this data in the indication of the type of delivery to be performed according to classification suggested by DGS 001/2015. Results: From 2016 to 2018. 5922 number of pregnancies resulted in 5922 of Robson Classifications. The variables with lower completeness were related to previous cesarean section (77%) and previous pregnancies (43%). After imputation, it fell to 3.9% and 0.56%, respectively causing 4.6% of discarded data from the total. Discussion: There is a significant amount of missing data in basic variables used to study the classification of delivery type. We believe that encouraging data completion with the possibility of comparing data between hospitals should be a priority in the health area.

Abstract
This article describes tsmp, an R package that implements the MP concept for TS. The tsmp package is a toolkit that allows all-pairs similarity joins, motif, discords and chains discovery, semantic segmentation, etc. Here we describe how the tsmp package may be used by showing some of the use-cases from the original articles and evaluate the algorithm speed in the R environment. This package can be downloaded at https://CRAN.R-project.org/package=tsmp.

Abstract
The missing data issue is often found in real-world datasets and it is usually handled with imputation strategies that replace the missing values with new data. Recently, generative models such as Variational Autoencoders have been applied for this imputation task. However, they were always used to perform the entire imputation, which has presented limited results when comparing to other state-of-the-art methods. In this work, a new approach called Variational Autoencoder Filter for Bayesian Ridge Imputation is introduced. It uses a Variational Autoencoder at the beginning of the imputation pipeline to filter the instances that are later fitted to a Bayesian ridge regression used to predict the new values. The approach was compared to four state-of-the-art imputation methods using 10 datasets from the healthcare context covering clinical trials, all injected with missing values under different rates. The proposed approach significantly outperformed the remaining methods in all settings, achieving an overall improvement between 26% and 67%.

Abstract
Missing data is an issue often addressed with imputation strategies that replace the missing values with plausible ones. A trend in these strategies is the use of generative models, one being Variational Autoencoders. However, the default loss function of this method gives the same importance to all data, while a more suitable solution should focus on the missing values. In this work an extension of this method with a custom loss function is introduced (Variational Autoencoder with Weighted Loss). The method was compared with state-of-the-art generative models and the results showed improvements higher than 40% in several settings.

Abstract