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

Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Innovations in Cancer Diagnostic and Decision Support (58)

Published on in Vol 7, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13476, first published .
A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

Authors of this article:

Jiangpeng Wu1, 2 Author Orcid Image ;   Xiangyi Zan3 Author Orcid Image ;   Liping Gao3 Author Orcid Image ;   Jianhong Zhao4 Author Orcid Image ;   Jing Fan2 Author Orcid Image ;   Hengxue Shi2 Author Orcid Image ;   Yixin Wan3 Author Orcid Image ;   E Yu5 Author Orcid Image ;   Shuyan Li1, 2 Author Orcid Image ;   Xiaodong Xie6 Author Orcid Image
Article Authors Cited by (20) Tweetations (3) Metrics

Journals

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  9. Li S, Li M, Wu J, Li Y, Han J, Cao W, Zhou X. Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM. EPMA Journal 2023;14(2):219 View
  10. Huang X, Xie B, Long J, Chen H, Zhang H, Fan L, Chen S, Chen K, Wei Y. Prediction of risk factors for scrub typhus from 2006 to 2019 based on random forest model in Guangzhou, China. Tropical Medicine & International Health 2023;28(7):551 View
  11. Zhai Y, Lin X, Wei Q, Pu Y, Pang Y. Interpretable prediction of cardiopulmonary complications after non-small cell lung cancer surgery based on machine learning and SHapley additive exPlanations. Heliyon 2023;9(7):e17772 View
  12. Choudhary A, Yu J, Kouznetsova V, Kesari S, Tsigelny I. Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites. Metabolites 2023;13(10):1055 View
  13. Bentick K, Runevic J, Akula S, Kyriacou T, Cool P, Andras P. Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy. Methods 2023;220:55 View
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Books/Policy Documents

  1. Bai F, Aruna S, Ashok Kumar S, Maheswari M, Katyal K, Vipat D, Parasar S. Decision-Making Models. View