Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients

Olivato, Matteo; Rossetti, Nicholas; Gerevini, Alfonso; Chiari, Mattia; Putelli, Luca; Serina, Ivan

doi:10.1016/j.procs.2022.09.179

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…20 In addition, some machine learning models artificially classified the negative conversion time into short-term (up to 7 days) and long-term (more than 7 days) and then used binary classification to predict the simplified negative conversion time. 22,23 However, our model can predict patients' actual negative conversion time, which is more instructive for clinicians.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have also constructed models for predicting the negative conversion time of COVID‐19 patients, but these models only considered several clinical features and did not take into account any laboratory tests, especially the vaccination status 20 . In addition, some machine learning models artificially classified the negative conversion time into short‐term (up to 7 days) and long‐term (more than 7 days) and then used binary classification to predict the simplified negative conversion time 22,23 . However, our model can predict patients' actual negative conversion time, which is more instructive for clinicians.…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that the Gradient Boosting model was superior to other machine models, but this study only used traditional machine models, and only the clinical characteristics of patients were used as models feature. Olivato et al 23 trained and tested several machine learning algorithms to predict whether the negative conversion time of COVID‐19 patients was more than 1 week. The model incorporated the vaccination status and laboratory results of the patients, but the distribution of patients in this study was longer, and different variants prevalence, vaccinations, and the introduction of new treatments can lead to concept drift issues that limit the effectiveness of machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

Shao

Yang

et al. 2023

Journal of Medical Virology

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

Shao

Yang

et al. 2023

Journal of Medical Virology

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“…In this systematic review of machine learning models related to the length of stay for COVID-19 patients, we identified and critically evaluated prediction models described in 10 studies. Nine [4,[23][24][25][26][27][28][29][30] of the prognostic models are qualitative and tried to predict the length of stay for COVID-19 patients in the shape of "Short" or" Long" and only one [31] of them predict the length of stay quantitatively. In the qualitative studies evaluation metrics for qualitative modeling such as accuracy, F1-score, specificity, sensitivity, and AUC have been reported and for the quantitative ones, the evaluation metrics were MAE, MSE, and MRE.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Machine Learning Algorithms to Predict the Length of Stay for COVID-19 Patients

Sayadi,

Sadeghian Yazdeli,

Asaadi Vaskas

et al. 2023

Front Health Inform

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Introduction: Managing resources is one of the most important challenges that healthcare providers worldwide face during the COVID-19 pandemic. In recent years, machine learning has been developed to provide valuable help in predicting disease and estimating the duration of their stay. This study aimed to identify the machine learning models for predicting length of stay in COVID-19.Material and Methods: Online databases, including Scopus, PubMed, Web of Science, and Science Direct, were searched, and a hand search through Google Scholar and grey literature was done up to August 2023 and updated in December 2023 to identify articles to find all relevant studies. To manage the process and check the quality of included articles PRISMA guidelines and CASP checklist were used and data was extracted using a data extraction form.Results: Among all 489 research articles, 10 studies met the inclusion criteria. The best models reported in the included articles were random forest (n=3), gradient boosting (n=2), XGBoost (n=2), SVM (n=1), KNN (n=1), and DataRobot (n=1). Except one of the studies that used quantitative modeling and reported MSE and MAE as evaluation criteria, other studies used qualitative modeling and reported accuracy, specificity, and F1-score. The focus of the included articles was on the general and ICU departments as the important resources in the hospital and emphasized the use of machine learning to predict the length of stay.Conclusion: The results of this systematic review showed that a data mining approach and using a machine learning algorithm can help to manage the critical resources of the hospital especially when we are faced with a pandemic disease like COVID-19.

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“…Returning to the topic of our work, the study of LOS, several works have been conducted in Italy. Scala et al [ 49 ], for example, use multiple linear regression and classification algorithms to predict the LOS of patients who accessed the hospital for a lower limb fracture, while Olivato et al [ 50 ] use machine learning algorithms to assess the LOS of hospitalized patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

et al. 2023

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Caesarean section (CS) rate has seen a significant increase in recent years, especially in industrialized countries. There are, in fact, several causes that justify a CS; however, evidence is emerging that non-obstetric factors may contribute to the decision. In reality, CS is not a risk-free procedure. The intra-operative, post-pregnancy risks and risks for children are just a few examples. From a cost point of view, it must be considered that CS requires longer recovery times, and women often stay hospitalized for several days. This study analyzed data from 12,360 women who underwent CS at the “San Giovanni di Dio e Ruggi D’Aragona” University Hospital between 2010 and 2020 by multiple regression algorithms, including multiple linear regression (MLR), Random Forest, Gradient Boosted Tree, XGBoost, and linear regression, classification algorithms and neural network in order to study the variation of the dependent variable (total LOS) as a function of a group of independent variables. We identify the MLR model as the most suitable because it achieves an R-value of 0.845, but the neural network had the best performance (R = 0.944 for the training set). Among the independent variables, Pre-operative LOS, Cardiovascular disease, Respiratory disorders, Hypertension, Diabetes, Haemorrhage, Multiple births, Obesity, Pre-eclampsia, Complicating previous delivery, Urinary and gynaecological disorders, and Complication during surgery were the variables that significantly influence the LOS. Among the classification algorithms, the best is Random Forest, with an accuracy as high as 77%. The simple regression model allowed us to highlight the comorbidities that most influence the total LOS and to show the parameters on which the hospital management must focus for better resource management and cost reduction.

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Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients

Cited by 7 publications

References 21 publications

Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

A Systematic Review on Machine Learning Algorithms to Predict the Length of Stay for COVID-19 Patients

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

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