Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Abstract

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 (n = 15,324; ≅70%) and test (n = 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.

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel

“…The binary categories (e.g., success and failure on an assessment in the psychometric field) have been applied in health-related outcomes. [ 13 – 17 ] However, none provided the animation-type dashboard showing on Google Maps in use for patients predicting the 5YSPBC, as we did in Fig. 3 .…”

“…The multi-classification module can be done by adding the layers on CNN. Any other types of self-assessment, such as predicting the 14-day hospital readmission of patients with pneumonia, [ 13 ] predicting active NBA players most likely to be inducted into the basketball hall of Famers, [ 14 ] and screening BC, [ 7 ] can apply the CNN model to predict and classify the levels of harmfulness and disease in the future.…”

“…First, the psychometric properties of the variables have been validated for predicting the 5YSPBC after removing invalidate variables from the 53 original categories; however, no evidence can support the 15-variable 5YSPBC viable and validate its use in our predictive models though correlation coefficients of 5YSPBC have been examined before involving them into models. Hence, additional studies are recommended using other selection techniques to select suitable feature variables (i.e., variables correlated to the label of survival and non-survival reaches 0.1 or above [ 13 ] ) in the future.…”

Comparison of Back-Propagation Neural Network, LACE Index and HOSPITAL Score in Predicting All-Cause Risk of 30-Day Readmission

“…In contrast to the usual interventions to prevent readmissions, such as medical reconciliation, patient education, arranging timely outpatient appointments, and providing telephone follow-up, reducing fatal readmissions or in-hospital mortality needs much more adequately and timely intensive, even futile care by health-care providers. 18 , 50 , 51 In such cases, where the unusual or interesting class is rare and as the class distribution becomes more skewed and to avoid unnecessary treatment or over-allocation of hospital resources due to a false-positive result, evaluation based on AUCPR 18 , 47 or a partial AUROC 52 may be preferred. The way to decide on which curve is better to optimize is context-dependent.…”

“…For example, in some cases such as in-hospital mortality or heart failure readmission, too many false positives appear in the more imbalanced-class data will inevitably increase the clinicians' burden. 18,50,51 Under such circumstances, the strategy to minimize the number of false positives, and thus the precision is of paramount importance. In contrast to the usual interventions to prevent readmissions, such as medical reconciliation, patient education, arranging timely outpatient appointments, and providing telephone follow-up, reducing fatal readmissions or in-hospital mortality needs much more adequately and timely intensive, even futile care by health-care providers.…”