A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records

Emergency room readmission, an avoidable problem? Analysis and stratification of readmissions in a trauma reference center

INTRODUCTION

Hospital readmission is a common way to assess the quality of care provided in an emergency service. In this context, the aim of this study is to quantify and stratify readmissions in a trauma reference emergency service.

METHODS

A retrospective longitudinal study was conducted with patients readmitted, twice or more, in the emergency service within a maximum period of 30 days from the initial admission - hospitalized or not. Clinical and demographic data were obtained from electronic medical records.

RESULTS

The readmission rate for the service was 4.11% for all readmissions and 2.23% for avoidable readmissions. Within this group, 61.19% were likely avoidable, 19.47% possibly avoidable, and 19.34% eventually avoidable. Regarding time, 48.16% occurred within one week of the initial readmission. Furthermore, no statistically significant association was found in the analysis of biological sex, occupational accident, and comorbidities. A statistically significant association was found in the analysis of age and ambulance transport (OR 1.37; 95% CI 1.17-1.59).

CONCLUSION

The study highlighted that there are still readmissions in the emergency department that could be avoided. A significant relationship was observed between readmissions and patient ages, and ambulance transport.

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

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.