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.

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.

Machine learning methods to predict presence of residual cancer following hysterectomy.

Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types.

Emergency Mail-in Voting in Rhode Island: Protecting Civic Participation During COVID-19 and Beyond.

The COVID-19 pandemic challenges safe and equitable voting in the United States' 2020 elections, and in response, several states including Rhode Island (RI) have made significant changes to election policy. In addition to increasing accessibility of mail-in voting by mailing applications to all registered voters, RI has suspended their notary/witness requirement for both the primary and general election. However, RI's "emergency" voting process still plays a crucial role in allowing voters who missed the mail-in ballot application deadline, such as those unexpectedly hospitalized in the days leading up to the election, to still cast their ballot.