AI Medical Lab

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Our team is made up of data analysts and clinicians who are working hard to combine cutting-edge AI technologies with clinical knowledge and translate these results into online models to help clinical practice.


A lncRNA-mRNA regulatory module of lung adenocarcinoma (LUAD)

Public gene expression data of three cohorts were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential expression analysis between LUAD and normal samples and survival analysis were performed. The protein-protein interaction (PPI) network and co-expression analysis were conducted as a further screening. The least absolute shrinkage and selection operator (LASSO) Cox regression model was developed to predict overall survival. Machine learning models were trained to recognize early-stage LUAD or EGFR-mutation.


Blood Transfusion

Risk prediction of RBC transfusion during or after liver transplantation

This model was successfully developed by using a machine learning algorithm, for predicting the risk of RBC transfusion during or after liver transplantation. Using seven preoperative variables, the model is convenient to use and performs well in predicting the risk of RBC transfusion and can guide high-risk patients to take appropriate preventive measures, before liver transplantation.

Liu LP*, Zhao QY*, Wu J, et al. Machine learning for the prediction of red blood cell transfusion in patients during or after liver transplantation surgery. Front Med; 2021. DOI: 10.3389/fmed.2021.632210.


Prediction of perioperative RBCs transfusion in pelvic fracture patients

Perioperative red blood cells (RBCs) transfusion is hard to predict accurately in patients with pelvic fracture. This study included the perioperative adult patients with pelvic trauma hospitalized in the six Chinese centers from September 2012 to June 2019. The data was split into training test (80%) and test set (20%). An extreme gradient boosting (XGBoost) algorithm was used to predict the need for perioperative RBCs transfusion.


Critical Care


The ALICE score (Acute myocardial ischemia, Lactate, Iliac arteries involved, and CreatininE are comprised) is a more reliable preoperative score for detecting fatality-prone patients than previously published preoperative risk scores for acute type A aortic dissection (aTAAD) in external validation, which might help bedside clinicians in early detection of the most severe aTAAD patients. 

Luo JC*, Zhong J*, Duan WX*, et al. Early risk stratification of acute type A aortic dissection: development and validation of a predictive score. Cardiovasc Diagn Ther; 2020. DOI: 10.21037/cdt-20-730.


Prediction of sepsis-induced coagulopathy (SIC) in critically ill patients with sepsis

This model was developed based on the MIMIC-IV database and externally validated on the eICU-CRD database. 15 features were selected according to feature importance and clinical availability. It could differentiate septic patients who would and would not develop SIC, with a AUC of more than 0.80 in validation.

Zhao QY*, Liu LP*, Luo JC*, et al. A machine-learning approach for dynamic prediction of sepsis-induced coagulopathy in critically ill patients with sepsis. Front Med; 2020. DOI: 10.3389/fmed.2020.637434.



Prediction of exintubation failure

Currently not public



Prediction of Diabetes Mellitus

Informal version


AI Medical Lab