Network effects in influenza spread: The impact of mobility and socio-economic factors.

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time.

Validation of a Machine Learning Model for Early Shock Detection.

Objectives: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review.

Design: We performed a single-center diagnostic performance study.

Patients And Setting: A prospective cohort consisted of consecutive patients aged 18 years and older who were admitted from May 1 through September 30, 2020 to six Mayo Clinic intensive care units (ICUs) and five progressive care units.

Me, Myself, and Not-I: Self-Discrepancy Type Predicts Avatar Creation Style.

In video games, identification with avatars-virtual entities or characters driven by human behavior-has been shown to serve many interpersonal and intraindividual functions (like social connection, self-expression, or identity exploration) but our understanding of the psychological variables that influence players' avatar choices remains incomplete. The study presented in this paper tested whether players' preferred style of avatar creation is linked to the magnitude of self-perceived discrepancies between who they are, who they aspire to be, and who they think they should be. One-hundred-and-twenty-five undergraduate gamers indicated their preferred avatar creation style and completed a values measure from three different perspectives: their actual, ideal, and ought selves.

Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.

Introduction: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation.

Materials And Methods: Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews.

"Give Me Proof": A Covert but Coercive Form of Non-partner Sexual Violence Contributing to Teen Pregnancy in Haiti and Opportunities for Biopsychosocial Intervention.

Adolescent girls in low-resource settings account for over 7.3 million births annually (generally unplanned). Unplanned teen pregnancies are increasing in low-resource settings.