Machine learning based assessment of preclinical health questionnaires.

Background: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data collected from questionnaires filled out by patients.

Vitamin Supplementation in Pre-Pregnancy and Pregnancy among Women-Effects and Influencing Factors in Romania.

Introduction: The aim of the study was to identify the consumption of vitamin and folic acid supplements before and during pregnancy in a group of post-partum women (Romanian, Hungarian, and Roma) from Mureș County, Romania, and the influence of socio-economic and behavioral factors on the consumption of vitamins. Materials and Methods: This cross-sectional questionnaire-based study included 1278 post-partum women (during the three days of hospitalization for birth), average age 29.5, registered for giving birth in the three hospitals in Mureș County, 2015−2016.