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Ying Che on Muck Rack

Ying Che

As seen in: Nature, Wiley Online Library, BMJ Open, Frontiers, Frontiers in Public Health, MDPI, Taylor & Francis Online, BMC Cancer, Science Magazine, Dove Medical Press and
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Articles

Retinal vascular parameters as AI-driven biomarkers for pulse wave velocity assessment: a telemedicine strategy for cardiovascular risk assessment

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By Yuelin Wang, Ying Che, Jinghao Qu
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Background Pulse wave velocity (PWV), known as the gold standard for evaluating arterial stiffness, is limited by device dependence.
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An interpretable machine learning model for predicting central lymph node metastasis in cN0 T1–T2 papillary thyroid carcinoma: a retrospective study

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By Yalin Zhu, Ying Che, Lina Wang, Shuhang Gao, Shuangsong Ren
| Frontiers
Abstract Introduction: This study aimed to develop and validate an interpretable machine learning model for preoperative prediction of central lymph node metastasis (CLNM) in patients with clinically node-negative (cN0) T1–T2 papillary thyroid carcinoma (PTC). Methods: A retrospective analysis was conducted on 710 patients (971 lesions), integrating pathological, ultrasound features, thyroid function, and systemic inflammatory indicators.
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The relationship between lifestyle, night shift pattern, mental health and the trajectories of nurses’ metabolic diseases: a cohort study of nurses

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By Min Yang, Ying Che, Jiaqi Xu, Xueqian Ma, Hongbo Chen, Baohua Li
| Frontiers in Public Health Verified
Abstract Objective: The study aimed to examine the relationship between lifestyle, night shift pattern, mental health and nurses’ metabolic diseases. Methods: We included 910 nurses from 2018 to 2022. The Growth mixture model was used to identify the trajectories of metabolic diseases among nurses. And multinomial logistic regression was used to examine the relationship between lifestyle, night shift pattern, mental health and the trajectories of metabolic diseases.
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