Key result
An integrated genetic algorithm and support vector machine model predicted 30-day all-cause readmission in pneumonia patients with an AUC of 0.7758, outperforming logistic regression and DNN models.
Why the study?
Precisely predicting hospital readmission is a global target, prompting the design of AI models to identify high-risk pneumonia patients with 30-day all-cause readmissions.
Does an integrated genetic algorithm and support vector machine model improve the prediction of 30-day all-cause readmission in pneumonia patients compared to logistic regression and deep neural networks?
Observational
Does an integrated genetic algorithm and support vector machine model improve the prediction of 30-day all-cause readmission in pneumonia patients compared to logistic regression and deep neural networks?
An integrated genetic algorithm and support vector machine model outperforms logistic regression and deep neural networks in predicting 30-day all-cause readmission for pneumonia patients.
May support AI readmission risk tools in pneumonia; leaves open prospective validation before clinical use.
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.
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Hsu et al. (2022) conducted an observational in Pneumonia. Integrated genetic algorithm and support vector machine (IGS) model vs. Logistic regression and deep neural network models was evaluated on 30-day all-cause readmission prediction (AUC). An integrated genetic algorithm and support vector machine model predicted 30-day all-cause readmission in pneumonia patients with an AUC of 0.7758, outperforming logistic regression and DNN models.
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