This paper is in the following e-collection/theme issue:

Machine Learning (1434) Clinical Information and Decision Making (1374) Decision Support for Health Professionals (1163) Breast Cancer (206)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19069, first published .
Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

Authors of this article:

Xiaorong Zhong1 Author Orcid Image ;   Ting Luo1 Author Orcid Image ;   Ling Deng2 Author Orcid Image ;   Pei Liu3 Author Orcid Image ;   Kejia Hu4 Author Orcid Image ;   Donghao Lu4 Author Orcid Image ;   Dan Zheng2 Author Orcid Image ;   Chuanxu Luo2 Author Orcid Image ;   Yuxin Xie1 Author Orcid Image ;   Jiayuan Li5 Author Orcid Image ;   Ping He1 Author Orcid Image ;   Tianjie Pu6 Author Orcid Image ;   Feng Ye6 Author Orcid Image ;   Hong Bu6 Author Orcid Image ;   Bo Fu3 Author Orcid Image ;   Hong Zheng2 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

Journals

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  2. Xiao J, Mo M, Wang Z, Zhou C, Shen J, Yuan J, He Y, Zheng Y. The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(2):e33440 View
  3. Suo J, Zhong X, He P, Zheng H, Tian T, Yan X, Luo T. A Retrospective Analysis of the Effect of Irinotecan-Based Regimens in Patients With Metastatic Breast Cancer Previously Treated With Anthracyclines and Taxanes. Frontiers in Oncology 2021;11 View
  4. Li H, Liu R, Long C, Teng Y, Cheng L, Liu Y. Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes. Cancer Management and Research 2022;Volume 14:909 View
  5. Massafra R, Fanizzi A, Amoroso N, Bove S, Comes M, Pomarico D, Didonna V, Diotaiuti S, Galati L, Giotta F, La Forgia D, Latorre A, Lombardi A, Nardone A, Pastena M, Ressa C, Rinaldi L, Tamborra P, Zito A, Paradiso A, Bellotti R, Lorusso V. Analyzing breast cancer invasive disease event classification through explainable artificial intelligence. Frontiers in Medicine 2023;10 View
  6. Istasy P, Lee W, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. Journal of Medical Internet Research 2022;24(11):e39748 View
  7. Wang J, Yin J, Qiu J, Jiang J, Hu Y, Zhu K, Zheng H, Luo T, Zhong X. Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study. Frontiers in Endocrinology 2022;13 View