Hospital Length of Stay Prediction using Regression Models

Grampurohit, Sneha; Sunkad, Sagar

doi:10.1109/inocon50539.2020.9298294

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Admission characteristics, such as admission source, day/month of admission, need for ICU admission, admitting unit, procedure type, time and length of last admission, elapsed LOS, and discharge/transfer destination, were used widely, possibly owing to the predominant use of medical record data sources and ongoing data collection throughout the admission period. Many studies using electronic medical records used information about the number of tests, consults, assessments, medication, and investigations as proxy indicators of extended stay rather than the actual results of these events ( 47 , 51 , 58 , 66 , 68 ).…”

Section: Resultsmentioning

confidence: 99%

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Gokhale,

Taylor,

Gill

et al. 2023

Front. Med.

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BackgroundUnwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.ObjectiveThis systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.MethodLOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.ResultsOverall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.ConclusionTo the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

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Section: Resultsmentioning

confidence: 99%

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Gokhale,

Taylor,

Gill

et al. 2023

Front. Med.

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show abstract

“…Grampurohit and Sunkad [4] used Ridge, Lasso, Linear regression and ElasticNet to predict the LOS of a patient from the day of his/her admission to the hospital to the day of his/her discharge. These researchers used Mean Absolute Error (MAE) to evaluate their models.…”

Section: Related Workmentioning

confidence: 99%

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

Mpia¹,

Kasolen²,

Baraka³

et al. 2023

Int.J.Res.Publ.Rev.

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In this research, the authors found that statistical analysis is very important preliminary phase in Machine Learning, especially for regression problems. Indeed, when the authors developed the first single models using the same algorithms and the same dataset, they obtained poor performances. After verifying the assumptions of the multiple linear regression, they adjusted the used data and produced efficient models. Moreover, as the objective was to apply the stacking model to predict Patient's Length of Stay in a semi urban hospital, the results showed that the stacking regressor performed better than the seven different models implemented (Random Forest, Extra Trees, Decision Tree, XGBoost, Multilayer perceptron, Light GBM, Support Vector Regressor (SVR)) taken individually. The authors combined Random Forest Regressor, Extra Trees Regressor, Decision Tree Regressor, XGBoost, Light GBM, and SVR to build the stacking model. Using secondary data from four services (Pediatrics, Hospitalization, Gynecology, and Neonatology) of a semi-urban hospital, located in a region of ongoing war in eastern Democratic Republic of Congo (DRC), the study examined the minimum length of stay of a patient in hospital when admitted in one of the four above services. Performances were evaluated using MAE, RMSE, MSE, R-squared and Accuracy. The stacking regression model shifted from 85% of accuracy before statistical analysis phase to 91% after applying statistics and from 0.75 to 0.91 as R-squared

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Machine Learning Methods for Prediction of COVID-19 Patient Length of Stay: Using Texas PUDF Data

Zhang,

2023

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Hospital Length of Stay Prediction using Regression Models

Cited by 5 publications

References 6 publications

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

Machine Learning Methods for Prediction of COVID-19 Patient Length of Stay: Using Texas PUDF Data

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