Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

Objective: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL).

Methods: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected.

Purification of Circulating Tumor Cells Based on Multiantibody-Modified Magnetic Nanoparticles and Molecular Analysis toward Epithelial Ovarian Cancer Detection.

Circulating tumor cells (CTCs) are valuable circulating biomarkers of cancer, which carry primary tumor information and may provide real-time assessment of tumor status as well as treatment response in cancer patients. Herein, we developed a novel assay for accurate diagnosis and dynamic monitoring of epithelial ovarian cancer (EOC) using CTC RNA analysis. Multiantibody-modified magnetic nanoparticles were prepared for purification of EOC CTCs from whole blood samples of clinical patients.

[Application of machine learning model based on XGBoost algorithm in early prediction of patients with acute severe pancreatitis].

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.

Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals.

Background: Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis.

Methods: This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers.