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

Machine Learning (3111) Public (e)Health, Digital Epidemiology and Public Health Informatics (2262) Chemotherapy and Side Effects Management (144) Lung Cancer (112) Cancer Prognosis Models and Machine Learning (136)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72424, first published .
Doctor in mask discusses patient results on computer screen

A Machine Learning Approach to Predicting Mortality Risk in Chemotherapy-Treated Lung Cancer: Machine Learning Model Development and Validation

A Machine Learning Approach to Predicting Mortality Risk in Chemotherapy-Treated Lung Cancer: Machine Learning Model Development and Validation

Authors of this article:

Jianjun Zou1, 2, 3 Author Orcid Image ;   Jinyi Huang4 Author Orcid Image ;   Katie Lu5 Author Orcid Image ;   Ao Lin4, 6, 7 Author Orcid Image ;   Chen Xie4 Author Orcid Image ;   Jinrong Zhang4 Author Orcid Image ;   Boqi Rao4, 6, 8 Author Orcid Image ;   Zhi Li4 Author Orcid Image ;   Dongming Xie4 Author Orcid Image ;   Ling Lu4 Author Orcid Image ;   Feng Luo4 Author Orcid Image ;   Jinbin Chen9 Author Orcid Image ;   Lei Yang4, 10, 11 Author Orcid Image ;   Fuman Qiu4 Author Orcid Image ;   Xin Zhang6, 7, 12 Author Orcid Image ;   Yibin Deng13 Author Orcid Image ;   Jiachun Lu4, 10, 11 Author Orcid Image
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Journals

  1. Zou Y, Zhu J, Cheng J, Yan J, Dai B, Han J, Guo J. Multi-source data-driven machine learning for lung cancer: diagnosis, treatment, and prognosis. Intelligent Opto-Electronics 2026;2(2):260006 View

Conference Proceedings

  1. Ghaleb M, ElSmahey M, Elsokkary H, Hamdy N. 2026 IEEE International Conference on Smart Sustainable Systems for Computer and Engineering Applications (3SCEA). SOX2OT as a Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer View