@inproceedings{36c5db3bdae249ba8dec3f5660aea256,
title = "Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases",
author = "Sheida Majouni and Kim, {Jong Sung} and Ellen Sweeney and Erin Keltie and Abidi, {Syed Sibte Raza}",
note = "Funding Information: This research was conducted using Atlantic PATH data and biosamples with funding from the NS Health Research Fund and the Canadian Cancer Society/New Brunswick Health Research Foundation. The data used in this research were made available by the Atlantic PATH study, which is the Atlantic Canada regional component of the CanPath funded by the Canadian Partnership Against Cancer and Health Canada. The views expressed herein represent the views of the authors and do not necessarily represent the views of Health Canada. Publisher Copyright: {\textcopyright} 2022 European Federation for Medical Informatics (EFMI) and IOS Press.; 32nd Medical Informatics Europe Conference, MIE 2022 ; Conference date: 27-05-2022 Through 30-05-2022",
year = "2022",
month = may,
day = "25",
doi = "10.3233/SHTI220385",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "3--7",
editor = "Brigitte Seroussi and Patrick Weber and Ferdinand Dhombres and Cyril Grouin and Jan-David Liebe and Jan-David Liebe and Jan-David Liebe and Sylvia Pelayo and Andrea Pinna and Bastien Rance and Bastien Rance and Lucia Sacchi and Adrien Ugon and Adrien Ugon and Arriel Benis and Parisis Gallos",
booktitle = "Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022",
address = "Netherlands",
}
