Addressing Bias in Feature Importance: A Hybrid Approach for Risk Prediction in Prognostic Survival Models

被引:1
|
作者
Takefuji, Yoshiyasu [1 ]
机构
[1] Musashino Univ, Fac Data Sci, Tokyo, Japan
关键词
D O I
10.1200/PO-24-00785
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
引用
收藏
页数:2
相关论文
共 50 条
  • [1] Reply to: Addressing Bias in Feature Importance: A Hybrid Approach for Risk Prediction in Prognostic Survival Models
    Zhang, Ge
    Zhang, Shiqian
    Zhang, Haonan
    Wu, Ruhao
    Zheng, Haoze
    JCO PRECISION ONCOLOGY, 2025, 9
  • [2] An approach to addressing selection bias in survival analysis
    Carlin, Caroline S.
    Solid, Craig A.
    STATISTICS IN MEDICINE, 2014, 33 (23) : 4073 - 4086
  • [3] Addressing bias in prediction models by improving subpopulation calibration
    Barda, Noam
    Yona, Gal
    Rothblum, Guy N.
    Greenland, Philip
    Leibowitz, Morton
    Balicer, Ran
    Bachmat, Eitan
    Dagan, Noa
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2021, 28 (03) : 549 - 558
  • [4] Hybrid approach for addressing uncertainty in risk assessments
    Guyonnet, D
    Bourgine, B
    Dubois, D
    Fargier, H
    Côme, B
    Chilès, JP
    JOURNAL OF ENVIRONMENTAL ENGINEERING, 2003, 129 (01) : 68 - 78
  • [5] SurvMaximin: Robust federated approach to transporting survival risk prediction models
    Wang, Xuan
    Zhang, Harrison G.
    Xiong, Xin
    Hong, Chuan
    Weber, Griffin M.
    Brat, Gabriel A.
    Bonzel, Clara-Lea
    Luo, Yuan
    Duan, Rui
    Palmer, Nathan P.
    Hutch, Meghan R.
    Gutierrez-Sacristan, Alba
    Bellazzi, Riccardo
    Chiovato, Luca
    Cho, Kelly
    Dagliati, Arianna
    Estiri, Hossein
    Garcia-Barrio, Noelia
    Griffier, Romain
    Hanauer, David A.
    Ho, Yuk-Lam
    Holmes, John H.
    Keller, Mark S.
    MEng, Jeffrey G. Klann
    L'Yi, Sehi
    Lozano-Zahonero, Sara
    Maidlow, Sarah E.
    Makoudjou, Adeline
    Malovini, Alberto
    Moal, Bertrand
    Moore, Jason H.
    Morris, Michele
    Mowery, Danielle L.
    Murphy, Shawn N.
    Neuraz, Antoine
    Ngiam, Kee Yuan
    Omenn, Gilbert S.
    Patel, Lav P.
    Pedrera-Jimenez, Miguel
    Prunotto, Andrea
    Samayamuthu, Malarkodi Jebathilagam
    Vidorreta, Fernando J. Sanz
    Schriver, Emily R.
    Schubert, Petra
    Serrano-Balazote, Pablo
    South, Andrew M.
    Tan, Amelia L. M.
    Tan, Byorn W. L.
    Tibollo, Valentina
    Tippmann, Patric
    JOURNAL OF BIOMEDICAL INFORMATICS, 2022, 134
  • [6] Reevaluating feature importance in machine learning for food authentication: Addressing bias and enhancing methodological rigor
    Takefuji, Yoshiyasu
    TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2025, 157
  • [7] Explaining Taxi Demand Prediction Models Based on Feature Importance
    Loff, Eric
    Schleibaum, Soeren
    Mueller, Joerg P.
    Saefken, Benjamin
    ARTIFICIAL INTELLIGENCE-ECAI 2023 INTERNATIONAL WORKSHOPS, PT 1, XAI3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, 2023, 2024, 1947 : 269 - 284
  • [8] Importance of Addressing Racial Heterogeneity Prior to Deploying Machine Learning Models for Postoperative Risk Prediction in Gastric Cancer
    Senthilkumar, Gopika
    Verhagen, Nathaniel B.
    Wolfrath, Nathan
    Xing, Yun
    Banerjee, Anjishnu
    Gehl, Carson J.
    Dream, Sophie
    Clarke, Callisia N.
    Maduekwe, Ugwuji N.
    Kothari, Anai
    JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS, 2024, 239 (05) : S446 - S447
  • [9] Prediction Of Readmission: ICU Risk & Prognostic Models
    Arsenault, K. A.
    Hamielec, C. M.
    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2010, 181
  • [10] A Robust Method to Measure the Global Feature Importance of Complex Prediction Models
    Zhang, Xiaohang
    Wu, Ling
    Li, Zhengren
    Liu, Huayuan
    IEEE ACCESS, 2021, 9 : 7885 - 7893