Majouni, S., Kim, J. S., Sweeney, E., Keltie, E., & Abidi, S. S. R. (2022). Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. In B. Seroussi, P. Weber, F. Dhombres, C. Grouin, J.-D. Liebe, J.-D. Liebe, J.-D. Liebe, S. Pelayo, A. Pinna, B. Rance, B. Rance, L. Sacchi, A. Ugon, A. Ugon, A. Benis, & P. Gallos (Eds.), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022 (pp. 3-7). (Studies in Health Technology and Informatics; Vol. 294). IOS Press BV. https://doi.org/10.3233/SHTI220385
Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. / Majouni, Sheida
; Kim, Jong Sung; Sweeney, Ellen et al.
Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. ed. / Brigitte Seroussi; Patrick Weber; Ferdinand Dhombres; Cyril Grouin; Jan-David Liebe; Jan-David Liebe; Jan-David Liebe; Sylvia Pelayo; Andrea Pinna; Bastien Rance; Bastien Rance; Lucia Sacchi; Adrien Ugon; Adrien Ugon; Arriel Benis; Parisis Gallos. IOS Press BV, 2022. p. 3-7 (Studies in Health Technology and Informatics; Vol. 294).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Majouni, S, Kim, JS, Sweeney, E, Keltie, E & Abidi, SSR 2022, Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. in B Seroussi, P Weber, F Dhombres, C Grouin, J-D Liebe, J-D Liebe, J-D Liebe, S Pelayo, A Pinna, B Rance, B Rance, L Sacchi, A Ugon, A Ugon, A Benis & P Gallos (eds), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. Studies in Health Technology and Informatics, vol. 294, IOS Press BV, pp. 3-7, 32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 5/27/22. https://doi.org/10.3233/SHTI220385
Majouni S, Kim JS, Sweeney E, Keltie E, Abidi SSR. Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. In Seroussi B, Weber P, Dhombres F, Grouin C, Liebe JD, Liebe JD, Liebe JD, Pelayo S, Pinna A, Rance B, Rance B, Sacchi L, Ugon A, Ugon A, Benis A, Gallos P, editors, Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. IOS Press BV. 2022. p. 3-7. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220385
Majouni, Sheida ; Kim, Jong Sung ; Sweeney, Ellen et al. / Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. editor / Brigitte Seroussi ; Patrick Weber ; Ferdinand Dhombres ; Cyril Grouin ; Jan-David Liebe ; Jan-David Liebe ; Jan-David Liebe ; Sylvia Pelayo ; Andrea Pinna ; Bastien Rance ; Bastien Rance ; Lucia Sacchi ; Adrien Ugon ; Adrien Ugon ; Arriel Benis ; Parisis Gallos. IOS Press BV, 2022. pp. 3-7 (Studies in Health Technology and Informatics).
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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,
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doi = "10.3233/SHTI220385",
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T1 - Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases
AU - Majouni, Sheida
AU - Kim, Jong Sung
AU - Sweeney, Ellen
AU - Keltie, Erin
AU - Abidi, Syed Sibte Raza
N1 - 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:
© 2022 European Federation for Medical Informatics (EFMI) and IOS Press.
PY - 2022/5/25
Y1 - 2022/5/25
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U2 - 10.3233/SHTI220385
DO - 10.3233/SHTI220385
M3 - Conference contribution
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AN - SCOPUS:85131105842
T3 - Studies in Health Technology and Informatics
SP - 3
EP - 7
BT - Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
A2 - Seroussi, Brigitte
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A2 - Dhombres, Ferdinand
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A2 - Pelayo, Sylvia
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A2 - Rance, Bastien
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A2 - Sacchi, Lucia
A2 - Ugon, Adrien
A2 - Ugon, Adrien
A2 - Benis, Arriel
A2 - Gallos, Parisis
PB - IOS Press BV
T2 - 32nd Medical Informatics Europe Conference, MIE 2022
Y2 - 27 May 2022 through 30 May 2022
ER -
