Acute pancreatitis （AP） is a gastrointestinal disease that requires early intervention， and when it progresses to moderate-severe AP （MSAP） or severe AP （SAP）， there will be a significant increase in the mortality rate of patients. Machine learning （ML） has achieved great success in the early prediction of AP using clinical data with the help of its powerful computational and learning capabilities. This article reviews the research advances in ML in predicting the severity， complications， and death of AP， so as to provide a theoretical basis and new insights for clinical diagnosis and treatment of AP through artificial intelligence.

OBJECTIVE: To analyze the results of noninvasive prenatal screening (NIPS) for fetal chromosome aneuploidy in twin pregnancy. METHODS: A total of 2057 women with twin-pregnancy between 12-26 weeks were recruited from Women's Hospital, Zhejiang University School of Medicine, Hangzhou Municipal Women's Hospital and Jiaxing Maternal and Child Health Hospital during February 2015 to August 2018. The cell-free DNA was extracted from the peripheral blood sample for DNA library, and non-invasive prenatal testing (NIPT) was performed by high-throughput sequencing technique. The fetal karyotype analysis or neonatal karyotype analysis was performed in pregnant women with fetal chromosome aneuploidy, and all subjects were followed up. The efficiency of NIPS testing for twin aneuploidy was calculated. RESULTS: NIPS revealed chromosome abnormalities in 11 out of 2057 twin pregnant women, 9 cases were confirmed chromosome abnormalities, 2 cases were normal and no false negative cases. In this screening, the detection rate, sensitivity, specificity, positive predictive value, false positive rate of NIPS were 100.00%, 100.00%, 99.90%, 81.82%, 0.10%. Those were 100.00%, 100.00%, 99.95%, 87.50% and 0.05% for trisomy 21, 100.00%, 100.00%, 100.00%, 100.00%, 0.00% for trisomy18, and the specificity and false positive rate for trisomy13 were 99.95% and 0.05%, respectively. CONCLUSIONS NIPS can detect fetal chromosomal aneuploidy rapidly and accurately in twin pregnancies,and it is of value in clinical application.
Aneuploidy ; Female ; Humans ; Noninvasive Prenatal Testing ; standards ; Pregnancy ; Pregnancy, Twin ; Prenatal Diagnosis ; methods ; Reproducibility of Results ; Trisomy

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms