Machine Learning to Predict ICU Admission, ICU Mortality and Survivors’ Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

Dan, Tingting; Yang, Li; Zhu, Ziwei; Chen, Xijie; Quan, Wuxiu; Hu, Yu; Tao, Guihua; Zhu, Li; Zhu, Jianmeng; Jin, Yuyan; Li, Longgeng; Liang, Chaokai; Wen, Huang; Cai, Hongmin

doi:10.1109/bibm49941.2020.9313292

Cited by 23 publications

(18 citation statements)

References 20 publications

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“… A risk prioritization tool that predicts the need for ICU admission within 24h to optimize the flow of operations within the hospitals. Dan et al, 2020 [ 13 ] Ensemble learning to objectively identify an optimal combination of factors that predicts ICU admissions across 733 COVID-19 patients. The number of lymphocytes was involved in all prediction tasks with the highest AUC score.…”

Section: Discussionmentioning

confidence: 99%

“…Ensemble-based algorithms, such as, the gradient boosting trees, have been widely used to predict 5-day ICU admission and 28-day mortality across 3597 COVID-19 patients, stressing the importance of CRP, LDH, and O2 saturation for ICU admission and neutrophil and lymphocyte percentages for mortality [ 12 ]. Ensemble learning has been deployed to identify an optimal combination of factors that predicts ICU admissions across 733 patients diagnosed with COVID-19 [ 13 ], as well as, across 1270 COVID-19 patients [ 14 ], highlighting the age, CRP, and LDH as prominent features for mortality. Furthermore, multipurpose machine learning algorithms (e.g., artificial neural networks and ensemble classifiers) have been proposed to estimate the risk of ICU admission or mortality among 3623 hospitalized patients with COVID-19, yielding a good discrimination performance [ 15 ], as well as, across 3280 patients to predict the risk of developing critical conditions in COVID-19 with high predictive performance [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

Pezoulas

Κούρου

Mylona

et al. 2022

Computers in Biology and Medicine

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The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

Pezoulas

Κούρου

Mylona

et al. 2022

Computers in Biology and Medicine

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“…The exact prediction of LOS can support the bed administration and project future requirements for optimal medical resource allocation. However, LOS may be affected by many elements and may be challenging, especially in complex, novel, and ambiguous medical conditions such as the current COVID-19 crisis (10,11). While statistical approaches have been applied to forecast hospital LOS but using ML algorithms is proven to have more optimal performance (44).…”

Section: Discussionmentioning

confidence: 99%

“…For this purpose, accurate forecasting of the COVID-19 hospital length of stay (LOS) and the determination of in uencing factors would be pivotal to optimal management and utilization of limited hospital resources. In addition, by predicting the LOS metrics, health care organizations could redesign their clinical pathways and recognize the bottlenecks for maximizing the use of medical resources (10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Orooji

Shanbehzadeh

Kazemi-Arpanahi³

et al. 2021

Preprint

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BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

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Discovering associations between radiological features and COVID‐19 patients' deterioration

Ahmadinejad

Ayyoubzadeh

Zeinalkhani

et al. 2023

Health Science Reports

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Background and AimsData mining methods are effective and well‐known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID‐19 by applying the rule mining method using characteristics of medical images.MethodsThis retrospective study has analyzed the radiological data from 104 COVID‐19 hospitalized patients diagnosed with COVID‐19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes.ResultsTen rules were extracted with only X‐ray‐related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan‐related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13.ConclusionThis study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID‐19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.

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Machine Learning to Predict ICU Admission, ICU Mortality and Survivors’ Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

Cited by 23 publications

References 20 publications

ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Discovering associations between radiological features and COVID‐19 patients' deterioration

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