Machine learning algorithms to predict 30-day readmission in patients with stroke: a prospective cohort study

Author:

Chen Yu-Ching1,Chung Jo-Hsuan1,Yeh Yu-Jo1,Lin Hsiu-Fen2,Lin Ching-Huang3,Hsien Hong-Hsi4,Hung Kuo-Wei5,Yeh Shu-Chuan Jennifer6,SHI HON-YI1ORCID

Affiliation:

1. Kaohsiung Medical University

2. Kaohsiung Medical University Chung Ho Memorial Hospital

3. Kaohsiung Veterans General Hospital

4. St. Joseph Hospital

5. Yuan's General Hospital

6. Sun Yat-Sen University

Abstract

Abstract Background No studies have discussed machine learning algorithms to predict the risk of 30-day readmission in patients with stroke. The objective of the present study was to compare the accuracy of the artificial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models and to explore the significant factors in predicting 30-day readmission after stroke. Methods This study prospectively compared the accuracy of the models using clinical data for 1,476 patients with stroke treated in six hospitals between March, 2014 and September, 2019. A training dataset (n=1,033) was used for model development, a testing dataset (n=443) was used for internal validation, and a validating dataset (n=167) was used for external validation. A global sensitivity analysis was performed to compare the significance of the selected input variables. Results Of all forecasting models, the ANN model had the highest accuracy in predicting 30-day readmission after stroke and had the highest overall performance indices. According to the ANN model, 30-day readmission was significantly associated with post-acute care (PAC) program, patient attributes, clinical attributes, and functional status scores before re-habilitation (all P <0.05). Additionally, PAC program was the most significant variable affecting 30-day readmission, followed by nasogastric tube insertion, and stroke type ( P <0.05). Conclusions Comparisons of the five forecasting models indicated that the ANN model had the highest accuracy in predicting 30-day readmission in stroke patients. Before stroke patients are discharged from hospitalization, they should be counseled regarding their potential for recovery and other possible outcomes. These important predictors can also be used to educate candidates for stroke patients who underwent PAC rehabilitation with respect to the course of recovery and health outcomes.

Publisher

Research Square Platform LLC

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Predictive modeling for COVID-19 readmission risk using machine learning algorithms;BMC Medical Informatics and Decision Making;2022-05-20

2. Post-Stroke Readmission Prediction Model Using Machine Learning Algorithms;Studies in Autonomic, Data-driven and Industrial Computing;2021

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