Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022)
Article
Article Title | Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022) |
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ERA Journal ID | 5031 |
Article Category | Article |
Authors | Sheehy, Joshua, Rutledge, Hamish, Acharya, U. Rajendra, Loh, Hui Wen, Gururajan, Raj, Tao, Xiaohui, Zhou, Xujuan, Li, Yuefeng, Gurney, Tiana and Kondalsamy-Chennakesavan, S. |
Journal Title | Artificial Intelligence in Medicine |
Journal Citation | 139 |
Article Number | 102536 |
Number of Pages | 12 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0933-3657 |
1873-2860 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.artmed.2023.102536 |
Web Address (URL) | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152133379&doi=10.1016%2fj.artmed.2023.102536&partnerID=40&md5=6b5313cf0f0cad59b737a35704c77d92 |
Abstract | Objective Methods Results Conclusion |
Keywords | Artificial intelligence; Prediction; Gynecological oncology; Machine learning; Prognosis |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Queensland |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
University of Southern Queensland | |
Queensland University of Technology |
https://research.usq.edu.au/item/z1w1z/gynecological-cancer-prognosis-using-machine-learning-techniques-a-systematic-review-of-the-last-three-decades-1990-2022
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