Readmission rates of patients with COVID-19 after hospital discharge

Alanlı, Recep; Küçükay, Murat Bülent; Yalcin, Kadir Serkan

doi:10.1590/1806-9282.20210675

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Readmission imposes additional costs on healthcare organizations and patients [22]. It also reduces the quality indicators of service delivery and raises the rate of serious complications and death during the pandemic [24,25].…”

Section: Open Accessmentioning

confidence: 99%

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

Shanbehzadeh

Yazdani

Shafiee

et al. 2022

BMC Med Inform Decis Mak

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Introduction The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. Results The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). Conclusions Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.

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Section: Open Accessmentioning

confidence: 99%

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

Shanbehzadeh

Yazdani

Shafiee

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Most people who acquire COVID-19 persist asymptomatic, indicating that human defenses are able to control this virus. However, some people who had manifested this disease, and despite having been vaccinated, may present new episodes of COVID-19, which are ascribed to a new contamination 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%