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Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review
5
Zitationen
23
Autoren
2024
Jahr
Abstract
This review examines current applications of ML in NCDs, highlighting potential biases and strategies for mitigation. Future research should focus on communicable diseases and the transferability of ML models in low and middle-income settings. Our findings can guide the development of guidelines for the equitable use of ML to improve population health outcomes.
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Autoren
- Sharon Birdi
- Roxana Rabet
- Steve Durant
- Atushi Patel
- Tina Vosoughi
- Mahek Shergill
- Christy Costanian
- Carolyn Ziegler
- Shehzad Ali
- David L. Buckeridge
- Marzyeh Ghassemi
- Jennifer Gibson
- Ava John‐Baptiste
- Jillian Macklin
- Melissa D. McCradden
- Kwame McKenzie
- Sharmistha Mishra
- Parisa Naraei
- Akwasi Owusu‐Bempah
- Laura C. Rosella
- James Shaw
- Ross Upshur
- Andrew D. Pinto
Institutionen
- Unity Health Toronto
- McMaster University(CA)
- St. Michael's Hospital(CA)
- Western University(CA)
- Public Health Ontario(CA)
- McGill University(CA)
- University of Toronto(CA)
- Hospital for Sick Children(CA)
- SickKids Foundation(CA)
- Wellesley Institute(CA)
- Toronto Metropolitan University(CA)
- Trillium Health Centre(CA)
- Centre for Health Equity Studies(SE)
- University of York(GB)
- Health Solutions (Sweden)(SE)
- St Michael's Hospital(GB)