Length-of-Stay Prediction for Pediatric Patients With Respiratory Diseases Using Decision Tree Methods

Ma, Fei; Yu, Limin; Ye, Lishan; Yao, David D.; Zhuang, Weifen

doi:10.1109/jbhi.2020.2973285

Cited by 36 publications

(30 citation statements)

References 37 publications

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“…Children with hematological diseases complicated by febrile neutropenia [31] Pediatric patients with respiratory diseases [2] Psychiatric unit or hospital Children [30], [25], [3] [20] 6…”

Section: Resultsmentioning

confidence: 99%

“…Half of the studies in our corpus used the procedure to validate model results, seeking its generalization. Cross-validation approaches are divided into three categories: traditional holdout [35], [20], [3], [41], [38], [27], [34], [2], temporal holdout [19], [23], [28], [39], [2], and k-fold [42], [28].…”

Section: Resultsmentioning

confidence: 99%

“…Traditional holdout randomly splits the dataset assuming that the frequency distributions do not change over time; temporal holdout divides the dataset taking into account the temporal evolution of the data. Except for two studies that do not mention the percentage of the dataset used in each partition [40], [2], all other studies used traditional holdout, varying the proportion of the dataset in the training and testing partitions, as follows: 80% -20% [27], [34], 75% -25% [35], 70% -30% [38], and 50% -50% [20], [3]. The k-fold method randomly divides the dataset into k unique parts of the same size, trains the forecast model with parts, and uses the remaining part for validation.…”

Section: Resultsmentioning

confidence: 99%

“…Other performance metrics reported were the Root Mean Square Error (RSME) and the Mean Absolute Error (MAE). RMSE measures the standard deviation of model errors; studies that used this metric reported values between 0.296 and 18.8 days [39], [2]. MAE measures the absolute average value of the differences between forecasts and actual observations; studies that used this metric reported values between 2.5 and 14.6 days [19], [39].…”

Section: Resultsmentioning

confidence: 99%

“…With the increasing demand for health services, hospitals worldwide are operating under pressure to increase patient care quality while ensuring organizational survival [1]. The hospital environment is complex and imposes several managerial challenges related to optimal utilization of limited resources and the constant need to improve e ciency and reduce patients' length-of-stay (LOS) [2]. Resource utilization planning requires predicting patients' LOS since longer times imply lower turnover and higher costs, affecting the quality of patient care and reducing the availability of services to the population [3].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Medeiros

Fogliatto

ROCHA

et al. 2021

Preprint

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Background: Healthcare management faces complex challenges in allocating hospital resources and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to identify approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS–P), and patients' populations and environments used to develop the models. Methods: Based on a scoping review, 28 studies were identified and analyzed. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation. Results: Forecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and mostly used in emergency departments to model patients in specific situations.Conclusions: The studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to methods applied for LOS–P forecasting and a future research agenda.

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“…Children with hematological diseases complicated by febrile neutropenia [31] Pediatric patients with respiratory diseases [2] Psychiatric unit or hospital Children [30], [25], [3] [20] 6…”

Section: Resultsmentioning

confidence: 99%