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

Clinical Information and Decision Making (1665) Machine Learning (1748) Artificial Intelligence (1754)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42435, first published .
Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study

Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study

Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study

Authors of this article:

Xue Wang1 Author Orcid Image ;   Fengchun Yang2 Author Orcid Image ;   Mingwei Zhu3, 4, 5 Author Orcid Image ;   Hongyuan Cui3, 4, 5 Author Orcid Image ;   Junmin Wei3, 4, 5 Author Orcid Image ;   Jiao Li2 Author Orcid Image ;   Wei Chen1 Author Orcid Image
Article Authors Cited by (8) Tweetations (7) Metrics

Journals

  1. Kang D, Kim H, Cho J, Kim Z, Chung M, Lee J, Nam S, Kim S, Yu J, Chae B, Ryu J, Lee S. Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning–Based Analysis. JMIR Public Health and Surveillance 2023;9:e45212 View
  2. Ma W, Cai B, Wang Y, Wang L, Sun M, Lu C, Jiang H. Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol. BMJ Open 2024;14(3):e077734 View
  3. Ma W, Cai B, Li H, Tan X, Deng M, Jiang L, Sun M, Jiang H. GLIM‐defined malnutrition in patients with acute abdomen associated with poor prognosis and increased economic burden: A cross‐sectional study. Nutrition in Clinical Practice 2024;39(6):1364 View
  4. Olshvang D, Harris C, Chellappa R, Santhanam P, Bonilla D. Predictive modeling of lean body mass, appendicular lean mass, and appendicular skeletal muscle mass using machine learning techniques: A comprehensive analysis utilizing NHANES data and the Look AHEAD study. PLOS ONE 2024;19(9):e0309830 View
  5. Liu Y, Huang L, Hu F, Zhang X. Investigating Frailty, Polypharmacy, Malnutrition, Chronic Conditions, and Quality of Life in Older Adults: Large Population-Based Study. JMIR Public Health and Surveillance 2024;10:e50617 View
  6. Rischmüller K, Caton V, Wolfien M, Ehlers L, van Welzen M, Brauer D, Sautter L, Meyer F, Valentini L, Wiese M, Aghdassi A, Jaster R, Wolkenhauer O, Lamprecht G, Bej S. Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters. Frontiers in Nutrition 2024;11 View
  7. Dai L, Yin J, Xin X, Yao C, Tang Y, Xia X, Chen Y, Lai S, Lu G, Huang J, Zhang P, Li J, Chen X, Zhong X. An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer. Cancer Imaging 2025;25(1) View
  8. Wang X, Li C, Xu L, Jiang S, Guan C, Che L, Wang Y, Man X, Xu Y. Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach. Clinical Kidney Journal 2025;18(4) View