Pulmonary Medicine, Chi-Mei Medical Center, Tainan 700, Taiwan.
Department of Medical Research, Chi-Mei Medical Center, Tainan 700, Taiwan.
Int J Environ Res Public Health. 2021 May 12;18(10):5110. doi: 10.3390/ijerph18105110.
Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training ( = 15,324; ≅70%) and test ( = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.
非计划性患者再入院（UPRA）在医疗保健环境中很常见且代价高昂。目前还没有向临床医生推荐任何住院期间的指标，以用于识别 UPRA 风险较高的患者。本研究旨在创建一种预测模型，用于早期检测肺炎患者的 14 天 UPRA。我们从台湾的三家医院下载了 2016 年至 2018 年以肺炎为主要疾病（例如，ICD-10：J12*-J18*）的患者数据。共收集了 21892 例（1208 例[6%]为 UPRA）。使用训练集（ = 15324；≈70%）和测试集（ = 6568；≈30%）比较了人工神经网络（ANN）和卷积神经网络（CNN）两种模型，以验证模型准确性。开发了一个用于预测和分类 UPRA 的应用程序。我们观察到：（i）在本研究中提取的 17 个特征变量使用 ANN 模型得出了高Receiver Operating Characteristic 曲线下面积（0.75），（ii）ANN 的 AUC（0.73）优于 CNN（0.50），（iii）开发了一个用于预测 UPRA 的即用型应用程序。该应用程序可以帮助临床医生在早期预测肺炎患者的 UPRA，并使他们能够在患者出院前后制定准备计划。
