Hospital Length of Stay Prediction Methods

Lequertier, Vincent; Wang, Tao; Fondrevelle, Julien; Augusto, Vincent; Duclos, Antoine

doi:10.1097/mlr.0000000000001596

Cited by 22 publications

(26 citation statements)

References 97 publications

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“…Therefore, improving LOS prediction with the best artificial intelligence method remains a key challenge, especially to enable better bed planning, care delivery and cost optimization. Linear and logistic regression methods have been supplanted by ML and deep learning (DL) models, yet it remains challenging to identify, benchmark and select optimal prediction methods given the discrepancy in data sources, inclusion criteria, choice of input variables, and metrics used [ 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…Scientific efforts to provide accurate prediction of LOS have been steady for half of a century [ 43 ]. While the use of ML in health-related research has become more and more popular, its application on LOS remains scattered.…”

Section: Discussionmentioning

confidence: 99%

“… The absence of separate datasets for training and assessment leading to overfitting (i.e., inflation of the model performance) [ 44 ]: model assessment must be implemented on a dataset never seen by the trained model. Selecting randomly a test-training split of the data set might lead to an overly optimistic or pessimistic outcome [ 29 , 43 ]. Hence, cross validation is recognized as an alternative.…”

Section: Discussionmentioning

confidence: 99%

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Machine-learning prediction for hospital length of stay using a French medico-administrative database

Jaotombo

Pauly

Fond

et al. 2022

Journal of Market Access & Health Policy

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Introduction: Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.Methods: Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90 th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC).Results: Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values <0.0001). The performance of the RF, GB and NN models (AUC ranged from 0.808 to 0.810) was superior to that of the LR model (AUC = 0.795); all p-values <0.0001. In contrast, LR was superior to CART (AUC = 0.786), p < 0.0001. The variable most predictive of the PLOS was the destination of the patient after hospitalization to other institutions. The typical clinical profile of these patients (17.5% of the sample) was the elderly patient, admitted in emergency, for a trauma, a neurological or a cardiovascular pathology, more often institutionalized, with more comorbidities notably mental health problems, dementia and hemiplegia.Discussion: The integration of ML, particularly the GB algorithm, may be useful for health-care professionals and bed managers to better identify patients at risk of PLOS. These findings underscore the need to strengthen hospitals through targeted allocation to meet the needs of an aging population.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine-learning prediction for hospital length of stay using a French medico-administrative database

Jaotombo

Pauly

Fond

et al. 2022

Journal of Market Access & Health Policy

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“…Numerous systematic reviews for prediction models have been published, and some of them focus on predicting outcomes relevant to health services research. For example, there are systematic reviews focused on prediction models for re-admission after an index hospitalization [ 23 , 24 ], emergency hospital admission [ 25 ], length of hospital stay [ 26 , 27 ], length of stay in the intensive care unit [ 28 ], and health care costs [ 29 ]. These systematic reviews summarize many prediction models, but their focus is on the data sources used, the predictors used and the model performance without providing a thorough assessment of reproducibility, transparency, and study quality.…”

Section: Main Textmentioning

confidence: 99%

Reproducibility of prediction models in health services research

Belbasis

Panagiotou

2022

BMC Res Notes

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The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research practices for health services research is important for evidence-based decision making. Several systematic reviews have summarized prediction models for outcomes relevant to health services research, but these systematic reviews do not present a thorough assessment of reproducibility and research quality of the prediction modelling studies. In the present commentary, we discuss how recent advances in prediction modelling in other medical fields can be applied to health services research. We also describe the current status of prediction modelling in health services research, and we summarize available methodological guidance for the development, update, external validation and systematic appraisal of prediction models.

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“…According to historical data, regression is the method that occupies the majority proportion of LOS prediction [ 15 ]. For example, Siddiqa et al [ 16 ] used multiple linear regression (MLR), decision tree regression (DTR), LR, RR, XGBR, and RFR techniques to predict LOS.…”

Section: Related Workmentioning

confidence: 99%

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

Zeng

2022

Computational Intelligence and Neuroscience

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The influx of hospital patients has become common in recent years. Hospital management departments need to redeploy healthcare resources to meet the massive medical needs of patients. In this process, the hospital length of stay (LOS) of different patients is a crucial reference to the management department. Therefore, building a model to predict LOS is of great significance. Five machine learning (ML) algorithms named Lasso regression (LR), ridge regression (RR), random forest regression (RFR), light gradient boosting machine (LightGBM), and extreme gradient boosting regression (XGBR) and six feature encoding methods named label encoding, count encoding, one-hot encoding, target encoding, leave-one-out encoding, and the proposed encoding method are used to construct the regression prediction model. The Scikit-Learn toolbox on the Python platform builds the prediction model. The input is the dataset named Hospital Inpatient Discharges (SPARCS De-Identified) 2017 with 2343569 instances provided by the New York State Department of Health verify the model after removing 2.2% of the missing data, and the model ultimately uses mean squared error (MSE) and coefficient of determination (R2) as the performance measurement. The results show that the model with the LightGBM algorithm and the proposed encoding method has the best R2 (96.0%) and MSE score (2.231).

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Hospital Length of Stay Prediction Methods

Cited by 22 publications

References 97 publications

Machine-learning prediction for hospital length of stay using a French medico-administrative database

Machine-learning prediction for hospital length of stay using a French medico-administrative database

Reproducibility of prediction models in health services research

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

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