Enhancing predictive models for sarcopenia: Suggestions for improved interpretability, feature inclusion, and stratified analyses

被引：1
|
作者：
Wei, Ruigang [1 ]
机构：
[1] Jiangxi Univ Finance & Econ, Sch Software & Internet Things Engn, Nanchang, Peoples R China
来源：
GERIATRICS & GERONTOLOGY INTERNATIONAL | 2024年 / 24卷 / 08期
关键词：
D O I：
10.1111/ggi.14927
中图分类号：
R592 [老年病学]; C [社会科学总论];
学科分类号：
03 ; 0303 ; 100203 ;
摘要：
引用
收藏
页码：818 / 818
页数：1
相关论文
共 9 条
  • [1] Incorporating advanced interpretability and expanded feature inclusions in predictive models for sarcopenia: Future directions
    Kim, Jun-Hee
    [J]. GERIATRICS & GERONTOLOGY INTERNATIONAL, 2024, 24 (08) : 819 - 820
  • [2] Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
    Sahad, Salbiah Mad
    Nian Wei Tan
    Sajid, Muhammad
    Jones, Ernest Austin, Jr.
    Latiff, Abdul Halim Abdul
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (18):
  • [3] Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis
    Contreras, Jhonatan
    Winterfeld, Andreea
    Popp, Juergen
    Bocklitz, Thomas
    [J]. ANALYTICAL CHEMISTRY, 2024, : 15588 - 15597
  • [4] Enhancing interpretability of tree-based models for downstream salinity prediction: Decomposing feature importance using the Shapley additive explanation approach
    Zhao, Guang-yao
    Ohsu, Kenji
    Saputra, Henry Kasmanhadi
    Okada, Teruhisa
    Suzuki, Jumpei
    Kuwahara, Yuji
    Fujita, Masafumi
    [J]. RESULTS IN ENGINEERING, 2024, 23
  • [5] Enhancing predictive accuracy for Cr(VI) removal in polymer inclusion membranes: A comparative study of machine learning models
    Fetimi, Abdelhalim
    Kebiche-Senhadji, Ounissa
    Benguerba, Yacine
    Albakri, Ghadah Shukri
    Alreshidi, Maha Awjan
    Abbas, Mohamed
    Hamachi, Mourad
    Bahita, Mohamed
    Merouani, Slimane
    Yadav, Krishna Kumar
    [J]. INORGANICA CHIMICA ACTA, 2024, 567
  • [6] Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data
    Kocbek, Primoz
    Fijacko, Nino
    Soguero-Ruiz, Cristina
    Mikalsen, Karl Oyvind
    Maver, Uros
    Brzan, Petra Povalej
    Stozer, Andraz
    Jenssen, Robert
    Skrovseth, Stein Olav
    Stiglic, Gregor
    [J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2019, 2019
  • [7] Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study
    Matthew R. Orton
    Evan Hann
    Simon J. Doran
    Scott T. C. Shepherd
    Derfel Ap Dafydd
    Charlotte E. Spencer
    José I. López
    Víctor Albarrán-Artahona
    Francesca Comito
    Hannah Warren
    Joshua Shur
    Christina Messiou
    James Larkin
    Samra Turajlic
    Dow-Mu Koh
    [J]. Cancer Imaging, 23
  • [8] Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study
    Orton, Matthew R.
    Hann, Evan J.
    Doran, Simon J.
    Shepherd, Scott T. C.
    Ap Dafydd, Derfel E.
    Spencer, Charlotte E.
    Lopez, Jose I.
    Albarran-Artahona, Victor
    Comito, Francesca
    Warren, Hannah
    Shur, Joshua
    Messiou, Christina
    Larkin, James
    Turajlic, Samra
    Koh, Dow-Mu
    [J]. CANCER IMAGING, 2023, 23 (01)
  • [9] Enhancing Predictive Models for Assessing 5G Exposure Effects on Human Health and Cognition through Supervised Machine Learning: A Multi-Stage Feature Selection Approach
    Sofri, Tasneem
    Andrew, Allan Melvin
    Rahim, Hasliza A.
    Nishizaki, Hiromitsu
    Kamarudin, Latifah Munirah
    Wong, Peng Wen
    Soh, Ping Jack
    [J]. PRZEGLAD ELEKTROTECHNICZNY, 2024, 100 (06): : 122 - 128
1