Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

Doğu, Elif; Albayrak, Y. Esra; Tuncay, Esin

doi:10.1007/s11517-021-02327-9

Cited by 15 publications

(8 citation statements)

References 33 publications

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“…Due to dramatic increases in healthcare costs and admission expenditures, accurate prediction of length of hospital stay and identify the risk factors of prolonged hospital stay helped physicians plan interventions in diagnosis and management for PLWHs with HIV-associated comorbidity, which was important to reduce waste of hospital resources (10,11).…”

Section: Introductionmentioning

confidence: 99%

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Li,

Hao,

Liu

et al. 2024

Front. Public Health

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ObjectiveThe study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.MethodsRegression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.ResultsIn regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.ConclusionThis study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.

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Section: Introductionmentioning

confidence: 99%

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Li,

Hao,

Liu

et al. 2024

Front. Public Health

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“…For example, Luo et al [7] used LR and RFC to predict LoS in patients with pulmonary disease. Likewise, Dogu et al [8] employed Artificial Neural Networks to predict LoS Chronic Obstructive Pulmonary Disease (COPD) patients while Kulkarni et al [9] performed it using Multi-Layer Perceptron (MLP) for acute coronary syndrome patients. Likewise, to predict the ICU admission, mortality, and survivors' LoS for COVID -19, Dan et al [10] created three ML prediction models without identifying cohorts of risk factors, and analysis was based only on univariate analysis.…”

Section: Introductionmentioning

confidence: 99%

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype

Alam¹,

Ananbeh²,

Malik³

et al. 2023

Preprint

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Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

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“…In another study, Ramkumar et al created an ML laboratory that was focused exclusively on orthopaedic surgery, with a two-fold aim: patient-specific value-based care, and human movement [45]. Moreover, interest in the use of ML to develop a predictive model of the hospital LOS has grown in recent years [46,47]. Researchers used a database composed of more than 120,000 patients to predict the LOS, measured in days, and the costs for patients who underwent a total hip and knee arthroplasty [48,49].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Karnuta et al developed a naïve Bayes ML algorithm and artificial neural networks to predict the LOS and costs for patients with fractures of the hip that used 103,592 patients [48,50,51]. More recently, Dogu et al [47,52,53] integrated statistically based fuzzy cognitive maps and artificial neural networks to build an LOS prediction model for patients with an acute exacerbation of chronic obstructive pulmonary disease.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture

et al. 2022

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Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R2) was achieved by the support vector machine (R2 = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.

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Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

Cited by 15 publications

References 33 publications

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype

Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture

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