Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Afrash, Mohammad Reza; Kazemi-Arpanahi, Hadi; Shanbehzadeh, Mostafa; Nopour, Raoof; Mirbagheri, Esmat

doi:10.1016/j.imu.2022.100908

Cited by 28 publications

(29 citation statements)

References 46 publications

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“…Mejia et al concluded that the lack of a valid and scientific model for predicting readmission of COVID-19 patients influences the higher mortality due to disease recurrence [82]. Afrash et al suggested the ML-based predictive models as useful for managing limited healthcare resources during the COVID-19 pandemic [83]. Donnely et al also stated that the prediction of COVID-19 readmission is a challenging but important task in preventing the devastating effects of disease recurrence or reinfection [22].…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

Shanbehzadeh

Yazdani

Shafiee

et al. 2022

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

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“…(7) has shown. This strand of methods has already emerged in the study of COVID-19 pandemic-related issues [ [42] , [43] , [44] ]. Now, for the more general situation, without any form of simplification, we assume the following functional form.…”

Section: Methodsmentioning

confidence: 99%

Modeling the optimization of COVID-19 pooled testing: How many samples can be included in a single test?

Liu

2022

Informatics in Medicine Unlocked

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“…40,41 This algorithm comprises three types of nodes (root, internal node and external node called leaf) in its tree structure. 1 Based on the tree structure, the internal node is a decision point and each leaf is a class. It uses a set of decision rules to assign data to classes.…”

Section: Dtmentioning

confidence: 99%

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Yazdani

Bigdeli

Zahmatkeshan

2023

Health Science Reports

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Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patients

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Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Cited by 28 publications

References 46 publications

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

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

Modeling the optimization of COVID-19 pooled testing: How many samples can be included in a single test?

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

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