Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer

Stoean, Ruxandra; Stoean, Cătălin; Săndiță, Adrian; Ciobanu, Daniela; Meşină, Cristian

doi:10.1007/978-3-319-19258-1_37

Cited by 17 publications

(9 citation statements)

References 12 publications

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“…CRC is one of the most common cancers affecting South Africans, and models that improve the care delivered to patients and simultaneously enhance efficiency for service providers are desperately needed (15,26). In this pilot study, the first of its kind for South Africa, we used machine learning models to determine the median LOS and investigated the factors impacting LOS for those with CRC undergoing surgical resection with curative intent.…”

Section: Discussionmentioning

confidence: 99%

“…The objective is to define a function [f(x) = y] that can correctly classify patients into one of the two classes based on the feature vector. LR is a statistical technique in which the response variable (y) has a binomial distribution (10,15). Given a set of features x i , LR regression determines the membership probability for one of the two classes using…”

Section: Logistic Regressionmentioning

confidence: 99%

“…Its application has been promoted in different studies due to its capacity to perform classification and regression based on statistical learning theory and structural risk minimization. Also, it has the ability to handle high-dimensional datasets and linear and nonlinear problems with high performance accuracy (15).…”

Section: Support Vector Machinementioning

confidence: 99%

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Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

et al. 2021

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The aim of this pilot study was to develop logistic regression (LR) and support vector machine (SVM) models that differentiate low from high risk for prolonged hospital length of stay (LOS) in a South African cohort of 383 colorectal cancer patients who underwent surgical resection with curative intent. Additionally, the impact of 10-fold cross-validation (CV), Monte Carlo CV, and bootstrap internal validation methods on the performance of the two models was evaluated. The median LOS was 9 days, and prolonged LOS was defined as greater than 9 days post-operation. Preoperative factors associated with prolonged LOS were a prior history of hypertension and an Eastern Cooperative Oncology Group score between 2 and 4. Postoperative factors related to prolonged LOS were the need for a stoma as part of the surgical procedure and the development of post-surgical complications. The risk of prolonged LOS was higher in male patients and in any patient with lower preoperative hemoglobin. The highest area under the receiving operating characteristics (AU-ROC) was achieved using LR of 0.823 (CI = 0.798–0.849) and SVM of 0.821 (CI = 0.776–0.825), with each model using the Monte Carlo CV method for internal validation. However, bootstrapping resulted in models with slightly lower variability. We found no significant difference between the models across the three internal validation methods. The LR and SVM algorithms used in this study required incorporating important features for optimal hospital LOS predictions. The factors identified in this study, especially postoperative complications, can be employed as a simple and quick test clinicians may flag a patient at risk of prolonged LOS.

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Section: Discussionmentioning

confidence: 99%

Section: Logistic Regressionmentioning

confidence: 99%

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Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

et al. 2021

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“…The model is used to regress the likely LOS in hospital of the patient. (Stoean et al 2015). In all of these studies, there is no consensus for defining a 'long-stay' patient.…”

Section: The Inpatient Settingmentioning

confidence: 99%

Machine learning in patient flow: a review

et al. 2021

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This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the demand and resource required to transfer patients from the emergency department to the hospital, prediction of potential resource required for the treatment and movement of inpatients and prediction of length-of-stay and discharge timing. We argue that there are benefits to both approaches of considering the healthcare institution as a whole as well as the patient by patient case and that ideally a combination of these would be best for improving patient flow through hospitals. We also argue that it is essential for there to be a shared dataset that will allow researchers to benchmark their algorithms on and thereby allow future researchers to build on that which has already been done. We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered. Future works should consider the need to transfer algorithms trained on a dataset to multiple hospitals and allowing for dynamic algorithms which will allow real-time decision-making to help clinical staff on the shop floor.

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“…Instead, it is more common to provide a range of days as the final result (frequently classified as a short, medium or long stay), defining these stays as specific intervals of days, e.g. in the way reported in [21] or [7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Length of Stay Across Hospital Departments

et al. 2021

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The length of hospital stay and its implications have a significant economic and human impact. As a consequence, the prediction of that key parameter has been subject to previous research in recent years. Most previous work has analysed length of stay in particular hospital departments within specific study groups, which has resulted in successful prediction rates, but only occasionally reporting predictive patterns. In this work we report a predictive model for length of stay (LOS) together with a study of trends and patterns that support a better understanding on how LOS varies across different hospital departments and specialties. We also analyse in which hospital departments the prediction of LOS from patient data is more insightful. After estimating predictions rates, several patterns were found; those patterns allowed, for instance, to determine how to increase prediction accuracy in women admitted to the emergency room for enteritis problems. Overall, concerning these recognised patterns, the results are up to 21.61% better than the results with baseline machine learning algorithms in terms of error rate calculation, and up to 23.83% in terms of success rate in the number of predicted which is useful to guide the decision on where to focus attention in predicting LOS.

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Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer

Cited by 17 publications

References 12 publications

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study

Machine learning in patient flow: a review

Predicting Length of Stay Across Hospital Departments

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