An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III

Abstract

Background. Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods. Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results. A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion. An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.

EASY‐APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

“…They found haematocrit, kinetic time (thromboelastogram), interleukin-6 and creatinine to have the greatest predictive power. Ding et al 54 used artificial neural networks and logistic regression for the early prediction of in-hospital mortality in AP. The authors used 12 variables that were collected within 24 h of admission from 337 patients.…”

“…They found haematocrit, kinetic time (thromboelastogram), interleukin‐6 and creatinine to have the greatest predictive power. Ding et al 54 …”

A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study

“…Data from patients diagnosed with AP were collected from the MIMIC-III through the computer code of International Classification of Diseases. AP was diagnosed based on the following conditions: (I) abdominal pain related to AP; (II) imaging evidence of AP through computed tomography (CT) scanning and/or ultrasonography; and (III) at least 3-fold increase of lipase and/or amylase levels compared with the normal threshold (13). Patients aged <18 years and those without clinical notes were excluded.…”

“…However, the predictive performance of the single laboratory indicator is likely to be affected by its fluctuation in accuracy. In addition, despite involvement of about 10 variables, SOFA and Ranson scores both need to be dynamically recorded and their application is limited in early prediction (13). Therefore, it is essential to establish a predictive model that has better accuracy when assessing the prognosis of AP.…”

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients

“…Random forests, k-nearest neighbors, support vector machines, decision trees and ensemble learning are also used in [33,[35][36][37][38]. While traditional machine learning approaches have been the norm in the clinical domain for years, newer mortality prediction studies have adopted deep learning-based approaches [39][40][41][42][43][44]. In [45], authors have used self normalizing neural network to predict 30-day mortality in ICU patients with an AUC of 0.8445, whereas [46] uses long short-term memory recurrent neural network to predict 12-hour mortality in ICU patients.…”