Abstract
A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.
Highlights
Readmission refers to patients who have been admitted to inpatient wards again after being discharged from hospitals within a short period of time
Health Insurance Research Database (NHIRD) with 20 features adopted, and we achieved a predictive performance with an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 69.33–71.44%, 66.27–69.41%, 69.32–72.24%, and 0.7518–0.7601, respectively
The data were retrieved from a subset including the claim data of 1 million patients randomly sampled from the NHIRD, containing information of medical facility registries, inpatient orders, ambulatory care, prescription drugs, and physicians providing services to the entire 23 million Taiwanese population enrolled in the NHI program
Summary
Readmission refers to patients who have been admitted to inpatient wards again after being discharged from hospitals within a short period of time. It may be attributed to unsuccessful treatments, new diseases, worsening comorbidities, or degraded quality of care [1], and can be caused by clinical and non-clinical factors [2,3], resulting in increased healthcare cost. The readmission rate is generally considered as an indicator for evaluating the healthcare quality of a hospital [4], it has been challenged that substantial errors were found when using it as a marker of healthcare quality [5]. In addition to improving hospital quality, the hospital readmission reduction program (HRRP) has been shown to be useful for reducing healthcare cost and elevating patient satisfaction [6].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
