Time-to-event machine learning prediction of metastatic recurrence of localized melanoma

被引:0
|
作者
Wan, G. [1 ,5 ]
Leung, B. [1 ]
DeSimone, M. [2 ]
Nguyen, N. [1 ]
Rajeh, A. [1 ]
Collier, M. [1 ]
Rashdan, H. [1 ]
Roster, K. [1 ]
Asgari, M. [1 ,5 ]
Gusev, A. [3 ]
Stagner, A. [1 ]
Lian, C.
Hurlbert, M. [4 ]
Yu, K. [5 ]
Tsao, H. [1 ,5 ]
Liu, F. [6 ]
Sorger, P. [5 ]
Semenov, Y. [1 ,5 ]
机构
[1] Massachusetts Gen Hosp, Boston, MA 02114 USA
[2] Brigham & Womens Hosp, 75 Francis St, Boston, MA 02115 USA
[3] Dana Farber Canc Inst, Boston, MA 02115 USA
[4] Melanoma Res Alliance, Washington, DC USA
[5] Harvard Med Sch, Boston, MA 02115 USA
[6] Stevens Inst Technol, Hoboken, NJ 07030 USA
关键词
D O I
暂无
中图分类号
R75 [皮肤病学与性病学];
学科分类号
100206 ;
摘要
217
引用
收藏
页码:S37 / S37
页数:1
相关论文
共 50 条
  • [31] Prediction of a time-to-event trait using genome wide SNP data
    Jinseog Kim
    Insuk Sohn
    Dae-Soon Son
    Dong Hwan Kim
    Taejin Ahn
    Sin-Ho Jung
    [J]. BMC Bioinformatics, 14
  • [32] A two-stage approach for dynamic prediction of time-to-event distributions
    Huang, Xuelin
    Yan, Fangrong
    Ning, Jing
    Feng, Ziding
    Choi, Sangbum
    Cortes, Jorge
    [J]. STATISTICS IN MEDICINE, 2016, 35 (13) : 2167 - 2182
  • [33] Calibrating Variations in Biomarker Measures for Improving Prediction with Time-to-event Outcomes
    Zheng, Cheng
    Zheng, Yingye
    [J]. STATISTICS IN BIOSCIENCES, 2019, 11 (03) : 477 - 503
  • [34] Prediction of a time-to-event trait using genome wide SNP data
    Kim, Jinseog
    Sohn, Insuk
    Son, Dae-Soon
    Kim, Dong Hwan
    Ahn, Taejin
    Jung, Sin-Ho
    [J]. BMC BIOINFORMATICS, 2013, 14
  • [35] A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes
    Wang, Xuechen
    Lee, Hyejung
    Haaland, Benjamin
    Kerrigan, Kathleen
    Puri, Sonam
    Akerley, Wallace
    Shen, Jincheng
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2024, 33 (05) : 794 - 806
  • [36] Shared random-effect models for the joint analysis of longitudinal and time-to-event data: application to the prediction of prostate cancer recurrence
    Sene, Mbery
    Bellera, Carine A.
    Proust-Lima, Cecile
    [J]. JOURNAL OF THE SFDS, 2014, 155 (01): : 134 - 155
  • [37] Deep learning based time-to-event prediction for a large multicentric cohort of H&N cancer patients
    Lombardo, E.
    Kurz, C.
    Marschner, S.
    Avanzo, M.
    Gagliardi, V.
    Fanetti, G.
    Franchin, G.
    Stancanello, J.
    Corradini, S.
    Niyazi, M.
    Belka, C.
    Parodi, K.
    Riboldi, M.
    Landry, G.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2021, 161 : S412 - S413
  • [38] MACHINE LEARNING TO PREDICT RECURRENCE OF LOCALIZED RENAL CELL CARCINOMA
    Guo, Yanbo
    Braga, Luis
    Kapoor, Anil
    [J]. JOURNAL OF UROLOGY, 2019, 201 (04): : E145 - E145
  • [39] Assessing slow-learning in MS with time-to-event data approach
    Luneau, C.
    Degraeve, B.
    Hautecoeur, P.
    Lenne, Bruno
    [J]. MULTIPLE SCLEROSIS JOURNAL, 2019, 25 (08) : NP15 - NP15
  • [40] A time-to-event analysis of the exposure-response relationship for bezlotoxumab concentrations and CDI recurrence
    Yee, Ka Lai
    Kleijn, Huub Jan
    Zajic, Stefan
    Dorr, Mary Beth
    Wrishko, Rebecca E.
    [J]. JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2020, 47 (02) : 121 - 130