基于 MIMIC-III 的急性胰腺炎院内死亡率早期预测的人工神经网络模型。
An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.
机构信息
Department of Emergency Medicine, The Second Xiangya Hospital, Emergency Medicine and Difficult Diseases Institute, Central South University, China.
Department of Emergency Medicine, Changsha Central Hospital, University of South China, China.
出版信息
Biomed Res Int. 2021 Jan 28;2021:6638919. doi: 10.1155/2021/6638919. eCollection 2021.
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.
背景
急性胰腺炎（AP）的严重程度和预后的早期、准确评估，尤其是在入院时非常重要。本研究旨在开发一种人工神经网络（ANN）模型，用于早期预测 AP 患者的住院死亡率。
方法
从医疗信息采集与临床决策支持系统 III 版（MIMIC-III）数据库中识别出 AP 患者。利用临床和实验室数据，通过反向传播 ANN 方法建立预测模型。
结果
本研究共分析了 337 例 AP 患者，住院死亡率为 11.2%。对存活组和非存活组患者之间存在差异的 12 个变量进行分析，构建 ANN 模型。多因素 logistic 回归分析确定了三个与住院死亡率相关的独立危险因素。基于受试者工作特征曲线下面积（AUC）的预测性能，ANN 模型为 0.769，logistic 回归为 0.607，Ranson 评分 0.652，SOFA 评分为 0.401。
结论
首次在 MIMIC-III 数据库中对 AP 患者的住院死亡率进行了 ANN 预测模型研究。通过我们的模型，可在 AP 早期轻松筛选出病死率高的患者。
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