We sought to evaluate whether there was variability in language used on social media across different time points of pregnancy (before, during, and after pregnancy, as well as by trimester and parity). Consenting patients shared access to their individual Facebook posts and electronic medical records. Random forest models trained on Facebook posts could differentiate first trimester of pregnancy from 3 months before pregnancy (F1 score = .
View Article and Find Full Text PDFAn amendment to this paper has been published and can be accessed via a link at the top of the paper.
View Article and Find Full Text PDFForecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (ED) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of Facebook statuses and electronic medical records.
View Article and Find Full Text PDFBackground: Pulmonary artery acceleration time measured by echocardiography inversely correlates with pulmonary artery pressures in adults and children older than 1 year of age. There is a paucity of data investigating this relationship in young children, particularly among preterm infants.
Objective: To characterize the relationship between pulmonary artery acceleration time (PAAT) and pulmonary artery pressures in infants.