Comparison of machine learning models for the prediction of hypertension in transgender patients undergoing gynecologic surgery.

Background: Transgender patients face a higher burden of cardiovascular morbidity due to structural and biological stressors, particularly in low-resource settings. No studies exist comparing machine learning model development strategies for this unique patient cohort and limited literature exists comparing data/outcomes between transgender and cisgender populations.

Methods: We compare machine learning models trained solely on transgender patients against models developed on a size-matched and ratio-matched cohort of cisgender patients and a 300-fold larger, ratio-matched cohort of cisgender patients undergoing obstetric/gynecologic procedures in the National Surgical Quality Improvement Program from January 1, 2005 through December 31, 2019.

Over 30 years of STEP: The Pittsburgh experience with first-episode psychosis.

Aims: For over 30 years, combined research and treatment settings in the US have been critical to conceptualizing care for first-episode psychosis (FEP). Here we describe an early example of such a context, the Services for the Treatment of Early Psychosis (STEP) clinic, which is affiliated with the University of Pittsburgh.

Methods: We describe STEP's historical roots and establishment in the early 1990s; STEP's research and treatment contributions, alongside its growth and ongoing leadership.

Novel Generative Recurrent Neural Network Framework to Produce Accurate, Applicable, and Deidentified Synthetic Medical Data for Patients With Metastatic Cancer.

Purpose: Sensitive patient data cannot be easily shared/analyzed, severely limiting the innovative progress of research, specifically for marginalized/under-represented populations. Existing methods of deidentification are subject to data breaches. The objective of this study was to develop a neural network capable of generating a synthetic version of data for patients with novel postoperative metastatic cancer.

Machine learning models to prognose 30-Day Mortality in Postoperative Disseminated Cancer Patients.

Patients with disseminated cancer at higher risk for postoperative mortality see improved outcomes with altered clinical management. Being able to risk stratify patients immediately after their index surgery to flag high risk patients for healthcare providers is vital. The combination of physician uncertainty and a demonstrated optimism bias often lead to an overestimation of patient life expectancy which can precent proper end of life counseling and lead to inadequate postoperative follow up.

Emergency Absentee Voting for Hospitalized Patients and Voting During COVID-19: A 50-State Study.

Introduction: Voters facing illness or disability are disproportionately under-represented in terms of voter turnout. Earlier research has indicated that enfranchisement of these populations may reinforce the implementation of policies improving health outcomes and equity. Due to the confluence of the coronavirus 2019 (COVID-19) pandemic and the 2020 election, we aimed to assess emergency absentee voting processes, which allow voters hospitalized after regular absentee deadlines to still obtain an absentee ballot, and election changes due to COVID-19 in all 50 states.