Predicting length of stay using regression and Machine Learning models in Intensive Care Unit: a pilot study

Picone, Ilaria; Latessa, Imma; Fiorillo, Antonella; Scala, Arianna; Trunfio, Teresa Angela; Triassi, Maria

doi:10.1145/3460238.3460247

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Our model revealed that most of the patients fell into the range categories of 0-7 days and more than 14 days using these two feature selection methods. Picone et al employed decision tree (DT), RF, gradient incremented tree (GBT), SVM, k nearest neighbor (KNN), multilayer MLP on 20% test data set to predict the ICU LOS and reported that RF performed better followed by MLP and SVM in predicting however, with a lower accuracy [31]. When using feature selection method infogain attribute selection evaluation +Ranker method, we also found that as the number of bins rose, the model's accuracy declined.…”

Section: Discussionmentioning

confidence: 99%

A data-driven approach to forecast the length of stay and overall treatment cost for resistant bacterial infections.

Chandra

Vandana

Mukhopadhyay

et al. 2022

Preprint

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The length of stay (LOS) and healthcare expenses for patients are drastically impacted by antimicrobial resistance (AMR). In addition to building a prediction model for AMR infection outcomes, the study will examine how AMR influences the attributable cost and length of stay in hospitalized patients. WEKA-ML version 3.8.6 was used to build the models. The discretization of LOS and cost into distinct bins is normalized. Utilizing a number of feature selection techniques, the best characteristics associated with the outcome were selected. The optimal feature selection strategy was selected, and several methods were used to the training (66 percent / 80 percent) and test (34 percent /20 percent) data sets to prevent underfitting and overfitting. Using ROC curves, prediction error, and accuracy metrics, the best-predicted model is selected. In terms of forecasting LOS, RF performed better (accuracy=69.6, ROC=0.852) than bagging (accuracy=69.6, ROC=0.862) while using the cfs subset attribute evaluation+greedy stepwise approach and the Infogain+ranker method. The majority of patients fell between the ranges of 7 and 14 days. With 34% of test data sets, RF outperformed marginally better using the infogain attribute selection+ranker technique (Accuracy=80.8 ROC=0.967) in predicting cost. Most fell into the >$1720 range, then came the $814 range. Effective LOS and treatment cost prediction for resistant infections gives crucial data that helps hospital administration, and the medical staff make crucial decisions. While avoiding a significant loss of resources, hospital administration can provide the appropriate and essential resources and the best medical team for treating the patient.

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Section: Discussionmentioning

confidence: 99%

A data-driven approach to forecast the length of stay and overall treatment cost for resistant bacterial infections.

Chandra

Vandana

Mukhopadhyay

et al. 2022

Preprint

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“…The use of machine learning models to support supervised classification tasks has been applied, achieving high efficacy performances in different domains (i.e., finance [55], security [56], music [57], and healthcare [58][59][60][61][62][63]). In particular, these methods mainly rely on two phases: feature selection, that has been discussed in Section 2.1, and classification.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Improta

Borrelli

Triassi

2022

IJERPH

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Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring—in a very short time—of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d’Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration.

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Studying length of stay in the Emergency Department of A.O.R.N. “Antonio Cardarelli” of Naples

Majolo

Ponsiglione

Longo

et al. 2021

2021 10th International Conference on Bioinformatics and Biomedical Science

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Predicting length of stay using regression and Machine Learning models in Intensive Care Unit: a pilot study

Cited by 4 publications

References 27 publications

A data-driven approach to forecast the length of stay and overall treatment cost for resistant bacterial infections.

A data-driven approach to forecast the length of stay and overall treatment cost for resistant bacterial infections.

Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Studying length of stay in the Emergency Department of A.O.R.N. “Antonio Cardarelli” of Naples

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