Chenyan Yu
10 Papers
Chenyan Yu is an academic researcher. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 2, co-authored 10 publications.
Chat about Author
Papers
Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals
Minyue Yin,Ru-Fu Zhang,Zhi Chao Zhou,Lu Liu,Jingwen Gao,Wei Xu,Chenyan Yu,Jiaxi Lin,Xiaolin Liu,Chunfang Xu,Jinzhou Zhu +10 more
TL;DR: The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability and outperformed other models in the training/validation dataset.
Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data
Yue Wang,Yu Hong,Yue Wang,Xin Zhou,Xin Gao,Chenyan Yu,Jiaxi Lin,Lu Liu,Jingwen Gao,Minyue Yin,Guoting Xu,Xiaolin Liu,Jinzhou Zhu +12 more
TL;DR: This study is the first to evaluate the feasibility of automated multimodal machine learning (MMML) in predicting 12-month EV bleeding based on endoscopic images and clinical variables.
18
Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study
Minyue Yin,Jiaxi Lin,Lu Liu,Jingwen Gao,Wei Xu,Chenyan Yu,Shuting Qu,Xiaolin Liu,Lijuan Qian,Chunfang Xu,Jinzhou Zhu +10 more
TL;DR: A DeepSurv model that performed well in CSS in SBT patients was reported, and patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K–M analysis.
Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis
Chenyan Yu,Yao Li,Minyue Yin,Jingwen Gao,Liting Xi,Jiaxi Lin,Lu Liu,Huixian Zhang,Airong Wu,Chunfang Xu,Xiaolin Liu,Yue Wang,Jinzhou Zhu +12 more
TL;DR: Wang et al. as mentioned in this paper evaluated the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis, and the best AutoML model was interpreted by SHapley Additive explanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME).
Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network–Based Deep Learning Models
Minyue Yin,Xiaolong Liang,Zilan Wang,Yijia Zhou,Yu He,Yuhan Xue,Jingwen Gao,Jiaxi Lin,Chenyan Yu,Lu Liu,Xiaolin Liu,Chao Xu,Jinzhou Zhu +12 more
TL;DR: Wang et al. as mentioned in this paper evaluated the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images and found that a transformer-based model, the Swin model, performed best.