Construction of a Multi-View Deep Learning Model for the Severity Classification of Acute Pancreatitis.
Journal:
Discovery medicine
PMID:
39851225
Abstract
BACKGROUND: Acute pancreatitis (AP) is a prevalent pathological condition of abdomen characterized by sudden onset, high incidence and complex progression. Timely assessment of AP severity is crucial for informing intervention decisions so as to delay deterioration and reduce mortality rates. Existing AP-related scoring systems can only assess current condition of patients and utilize only a single type of clinical data, which is of great limitation. Therefore, it is imperative to establish more accurate and data-compatible methods for predicting the severity of AP. The artificial intelligence (AI) algorithm based on artificial neural network (ANN) allow for the adaptive feature extraction for objective task through its internal complex network, instead of the hand-crafted methods commonly used in traditional machine learning (ML) algorithms. In this study, we delve into the final severity classification prediction of newly admitted AP patients, using deep learning (DL) algorithm to develop multi-view models, incorporated with patients' demographic information, vital signs, AP-related laboratory indexes and admission computed tomography (CT) images.