The Identification of Prolonged Length of Stay for Surgery Patients

Chuang, Mao-Te; Hu, Ya-Han; Tsai, Chia-Wei; Lo, Chi-Fang; Lin, Wei-Chiang

doi:10.1109/smc.2015.522

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…5. Other authors obtain better results with Random Forest algorithm as stated in [12] and [5]. In our study, the Random Forest algorithm obtained the best mean predicted value in all hospital departments which is also in concordance with these two articles.…”

Section: Discussionsupporting

confidence: 90%

“…Other studies -e.g. [5] -confirm our choice, as their results indicate that the random forest method in surgery patients is the most accurate and stable prediction model among all the methods analysed.…”

Section: Resultssupporting

confidence: 70%

“…From the hospital departments perspective, both [1] and [5] analyse General Surgery department getting the best results with Decision Tree and Random Forest algorithms, respectively. Our research results align with them because all surgery departments also obtained the best results with these two algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…An algorithm often used is Random Forest, which offers similar results to Decision Trees in those cases that data maintains the appropriate balance between bias and variance. For example, see [5]. In [12] also compare the Random Forest algorithm and neural networks, obtaining better results with the first one.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 90%

Section: Resultssupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Length of Stay Across Hospital Departments

et al. 2021

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“…The model considers diagnosis details of the patient to perform the predictions. Other similar techniques used for forecasting the length of stay based on threshold values include works bySotoodeh et al [24], andChuang et al [25].…”

Section: Related Workmentioning

confidence: 99%

Feature augmented stacked bagging for length of stay prediction on mimic III

Geethamani¹,

Rangaraj²

2022

ijhs

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Managing hospital resources is one of the major requirements in providing qualitative and timely treatments do patients. From the patient’s standpoint, identifying the number of days of stay can aid in effective planning from the monetary perspective.This work presents the Feature Augmented Stacked Bagging (FASB) model for effective prediction of length of stay of patient based on their hospital records. The ensemble model created is composed of two levels; the first level operates on the hospital data and provides the intermediate predictions, while the second level uses predictions from the first level model to perform the final predictions. Experiments where performed using the MIMIC III data, and the results obtained were compared with existing state of the art models for analysis. Comparisons indicate that the proposed model exhibits 95% accuracy levels, which is 13% greater than the compared model. This indicates that the proposed model is highly effective in identifying the length of stay of patients.

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