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A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation

A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation

For example, a study by Liu et al [33] suggested that AGE and NEU% could serve as predictive factors for the severity of viral pneumonia. Shen et al [31] also reported that RDW-SD is a key indicator for assessing the severity of viral pneumonia. Additionally, CRP and AGE play crucial roles in the diagnosis of viral pneumonia [54,55], which is consistent with the findings of this study.

Xiaojie Jin, Yanru Wang, Jiarui Wang, Qian Gao, Yuhan Huang, Lingyu Shao, Jiali Zhao, Jintian Li, Ling Li, Zhiming Zhang, Shuyan Li, Yongqi Liu

JMIR Med Inform 2025;13:e64725

Authors’ Response to Peer Review of “Using Electrooculography and Electrodermal Activity During a Cold Pressor Test to Identify Physiological Biomarkers of State Anxiety: Feature-Based Algorithm Development and Validation Study”

Authors’ Response to Peer Review of “Using Electrooculography and Electrodermal Activity During a Cold Pressor Test to Identify Physiological Biomarkers of State Anxiety: Feature-Based Algorithm Development and Validation Study”

Biomedical Engineering, Li U-IMT-EX-351 Linköping 2003. Available: https://www.diva.portal.org/smash/get/diva2:673960/FULLTEXT01.pdf Test.” Response: This is now reference 16. This was corrected. Reference 19: The correct citation should be “Anxiety Detection Using Multimodal Physiological Sensing, 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece, 2021, pp. 1-4, doi: 10.1109/BHI50953.2021.9508589.” Response: This is now reference 25. This was corrected.

Jadelynn Dao, Ruixiao Liu, Sarah Solomon, Samuel Aaron Solomon

JMIRx Med 2025;6:e77440