Open Access. Powered by Scholars. Published by Universities.®
Health Information Technology Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Health Information Technology
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
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 …
Consensus Statement On Ethical & Safety Practices For Conducting Digital Monitoring Studies With People At Risk Of Suicide And Related Behaviors, Matthew K. Nock, Evan M. Kleiman, Melissa Abraham, Kate H. Bentley, David A. Brent, Ralph J. Buonopane, Franckie Castro-Ramirez, Christine B. Cha, Walter Dempsey, John Draper, Catherine R. Glenn, Jill Harkavy-Friedman, Michael R. Hollander, Jeffrey C. Huffman, Hye In S. Lee, Alexander J. Millner, David Mou, Jukka-Pekka Onnela, Rosalind W. Picard, Heather M. Quay, Osiris Rankin, Shannon Sewards, John Torous, Joan Wheelis, Ursula Whiteside, Galia Siegel, Anna E. Ordóñez, Jane L. Pearson
Consensus Statement On Ethical & Safety Practices For Conducting Digital Monitoring Studies With People At Risk Of Suicide And Related Behaviors, Matthew K. Nock, Evan M. Kleiman, Melissa Abraham, Kate H. Bentley, David A. Brent, Ralph J. Buonopane, Franckie Castro-Ramirez, Christine B. Cha, Walter Dempsey, John Draper, Catherine R. Glenn, Jill Harkavy-Friedman, Michael R. Hollander, Jeffrey C. Huffman, Hye In S. Lee, Alexander J. Millner, David Mou, Jukka-Pekka Onnela, Rosalind W. Picard, Heather M. Quay, Osiris Rankin, Shannon Sewards, John Torous, Joan Wheelis, Ursula Whiteside, Galia Siegel, Anna E. Ordóñez, Jane L. Pearson
Psychology Faculty Publications
OBJECTIVE: Digital monitoring technologies (e.g., smart-phones and wearable devices) provide unprecedented opportunities to study potentially harmful behaviors such as suicide, violence, and alcohol/substance use in real-time. The use of these new technologies has the potential to significantly advance the understanding, prediction, and prevention of these behaviors. However, such technologies also introduce myriad ethical and safety concerns, such as deciding when and how to intervene if a participant's responses indicate elevated risk during the study?
METHODS: We used a modified Delphi process to develop a consensus among a diverse panel of experts on the ethical and safety practices for conducting digital …