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) |
|---|---|
| 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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