Health Information Technology Commons^™

Architectural Design Of A Blockchain-Enabled, Federated Learning Platform For Algorithmic Fairness In Predictive Health Care: Design Science Study, Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead …

Virtual Home Visits During Covid-19 Pandemic: Mothers' And Home Visitors' Perspectives, Abdullah Al-Taiar, Michele A. Kekeh, Stephanie Ewers, Amy L. Prusinski, Kimberly J. Alombro, Nancy Welch

Community & Environmental Health Faculty Publications

Background

The experiences of mothers enrolled in Maternal, Infant and Early Childhood Home Visiting (MIECHV) program with virtual home visiting (VHV) during the pandemic remain mostly unknown. This study aimed to describe in detail the experience of home visitors and mothers with VHV during COVID-19 pandemic. This is a prerequisite for guiding future efforts to optimize MIECHV services that are provided through virtual operation.

Methods

Focus groups discussion were conducted with home visitors (n = 13) and mothers (n = 30) who were enrolled in BabyCare program in Virginia from January 2019 to June 2022. This included mothers who received …