Research on Data Science Ensembles for Covid-19 Detection and Length of Stay Prediction

Sinha, Shubham; Tushar,; Goel, Shalini

doi:10.1109/icccis51004.2021.9397218

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…Using datasets containing Covid-19 patient data, Sinha, Tushar, and Goel [6] used catboost as algorithm to predict the maximum duration of a patient's stay in a hospital. Their goal was to help hospitals quickly manage hospital resources such as beds and medications.…”

Section: Related Workmentioning

confidence: 99%

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

Mpia¹,

Kasolen²,

Baraka³

et al. 2023

Int.J.Res.Publ.Rev.

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In this research, the authors found that statistical analysis is very important preliminary phase in Machine Learning, especially for regression problems. Indeed, when the authors developed the first single models using the same algorithms and the same dataset, they obtained poor performances. After verifying the assumptions of the multiple linear regression, they adjusted the used data and produced efficient models. Moreover, as the objective was to apply the stacking model to predict Patient's Length of Stay in a semi urban hospital, the results showed that the stacking regressor performed better than the seven different models implemented (Random Forest, Extra Trees, Decision Tree, XGBoost, Multilayer perceptron, Light GBM, Support Vector Regressor (SVR)) taken individually. The authors combined Random Forest Regressor, Extra Trees Regressor, Decision Tree Regressor, XGBoost, Light GBM, and SVR to build the stacking model. Using secondary data from four services (Pediatrics, Hospitalization, Gynecology, and Neonatology) of a semi-urban hospital, located in a region of ongoing war in eastern Democratic Republic of Congo (DRC), the study examined the minimum length of stay of a patient in hospital when admitted in one of the four above services. Performances were evaluated using MAE, RMSE, MSE, R-squared and Accuracy. The stacking regression model shifted from 85% of accuracy before statistical analysis phase to 91% after applying statistics and from 0.75 to 0.91 as R-squared

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Section: Related Workmentioning

confidence: 99%

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

Mpia¹,

Kasolen²,

Baraka³

et al. 2023

Int.J.Res.Publ.Rev.

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“…During this time, most healthcare settings have experienced with capacity reduction due to high referral volumes and bed occupancy as well as prolonged hospitalization [41]. The exact prediction of LOS can support the bed administration and projecting future requirements for optimal medical resource allocation [17,21]. Predicting hospital bed request (as well as associated medical resources) offer key evidence for hospital staffing and resource planning decisions.…”

Section: Performance Evaluation Of Modelsmentioning

confidence: 99%

“…It is significant for clinicians and health policymakers to make proper decisions for allocating of restricted resources [21,42]. Using ML based prediction models (intelligence system) is proven to be useful for optimum LOS estimation.…”

Section: Performance Evaluation Of Modelsmentioning

confidence: 99%

“…In this study, 53 primary features are reduced to 20 by using the correlation coefficient at the P-value< 0.2. In the bibliography, some studies have been undertaken to identify the key risk factors for COVID-19 hospital LOS [17, 21,42]. The top clinical variables affecting longer LOS in reviewed studies included age (basic data), cardiovascular diseases and hypertension (underline diseases), fever and low oxygen saturation (manifestations), leukocytosis (immunological), pulmonary lesion (radiological), mechanical ventilation (oxygen therapy) and increased BUN.…”

Section: Performance Evaluation Of Modelsmentioning

confidence: 99%

“…In addition, health care organizations could help the design of wellorganized clinical pathways and recognize the bottlenecks to improve resource utilization, resource allocation proactively, and better healthcare supply chain management [16,17]. It is significant for hospital administrator, clinicians and health policymakers to make proper decisions for allocating of restricted medical resources particularly during current COVID-19 pandemic [17,21]. Given this context, the ability to identify patients who are at risk for prolonged LOS during their hospitalization episode may be useful for identifying and prioritizing patient requirements, care planning, and optimizing service delivery [22].…”

Section: Introductionmentioning

confidence: 99%

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Untitled

Afrash

Kazemi-Arpanahi²,

Ranjbar

et al. 2021

J. Med. Chem. Sci.

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The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-19 patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of 1225 laboratory-confirmed COVID-19 hospitalized patients from February 9, 2020, to December 20, 2020. The most important clinical parameters in the COVID-19 LOS prediction were identified with a correlation coefficient at the P-value< 0.2. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of 20 variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, and the standard deviation of 1.2. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-19 patients and potentially facilitates hospital bed management, turnover and optimized resource allocation.

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Machine learning-based prediction of length of stay (LoS) in the neonatal intensive care unit using ensemble methods

Erdogan Yildirim,

Canayaz

2024

Neural Comput & Applic

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Neonatal medical data holds critical information within the healthcare industry, and it is important to analyze this data effectively. Machine learning algorithms offer powerful tools for extracting meaningful insights from the medical data of neonates and improving treatment processes. Knowing the length of hospital stay in advance is very important for managing hospital resources, healthcare personnel, and costs. Thus, this study aims to estimate the length of stay for infants treated in the Neonatal Intensive Care Unit (NICU) using machine learning algorithms. Our study conducted a two-class prediction for long and short-term lengths of stay utilizing a unique dataset. Adopting a hybrid approach called Classifier Fusion-LoS, the study involved two stages. In the initial stage, various classifiers were employed including classical models such as Logistic Regression, ExtraTrees, Random Forest, KNN, Support Vector Classifier, as well as ensemble models like AdaBoost, GradientBoosting, XGBoost, and CatBoost. Random Forest yielded the highest validation accuracy at 0.94. In the subsequent stage, the Voting Classifier—an ensemble method—was applied, resulting in accuracy increasing to 0.96. Our method outperformed existing studies in terms of accuracy, including both neonatal-specific length of stay prediction studies and other general length of stay prediction research. While the length of stay estimation offers insights into the potential suitability of the incubators in the NICUs, which are not universally available in every city, for patient admission, it plays a pivotal role in delineating the treatment protocols of patients. Additionally, the research provides crucial information to the hospital management for planning such as beds, equipment, personnel, and costs.

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Research on Data Science Ensembles for Covid-19 Detection and Length of Stay Prediction

Cited by 3 publications

References 27 publications

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

Untitled

Machine learning-based prediction of length of stay (LoS) in the neonatal intensive care unit using ensemble methods

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