An integrated optimization and machine learning approach to predict the admission status of emergency patients

Ahmed, Abdulaziz; Ashour, Omar; Ali, Haneen; Firouz, Mohammad

doi:10.1016/j.eswa.2022.117314

Cited by 12 publications

(2 citation statements)

References 69 publications

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“…Fifty-four models are developed and compared and found to show great promise in predicting patient needs, possibly alleviating crowding issues. 65…”

Section: Machine Learning To Predict Patient Admission Statusmentioning

confidence: 99%

Interventions Intended to Alleviate Emergency Department Overcrowding

Mason,

Secord,

MacDougall

2023

cjht

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Why Is This an Issue? Emergency department (ED) overcrowding is a known issue in Canada that puts patients’ lives and health at risk when treatment needs within the ED exceed the resources required to address them. The causes and consequences of ED overcrowding are complex, varied, and extend beyond the ED. Left unchecked, ED overcrowding contributes to a deteriorating standard of care as staff become overworked and burned out. What Are the Potential Interventions to Address ED Overcrowding? This roundup of 87 new and emerging interventions intended to alleviated ED overcrowding complements CADTH’s Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions. Interventions were included in this report if they are not captured in CADTH’s complimentary report Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions because they are either new or are not yet in wide use in EDs and health systems across Canada and could be reasonably be expected to affect ED overcrowding (e.g., by improving patient flow through the ED or reducing the number of people seeking care in the ED). Interventions that could address multiple causal factors (e.g., e-consult services) were also included. What Is the Potential Impact? Decreasing ED overcrowding has the potential to improve the standard of care delivered to those accessing EDs and contribute to decreasing the burden on ED staff, regardless of the intervention or which contributing factor it aims to act upon. Some interventions that can alleviate ED overcrowding also have an impact on primary and community care; bolstering preventive care, increasing access to diagnostic testing for those who need it, and improving access to health care supports can improve health outcomes. What Else Do We Need to Know? The pan-Canadian issue of ED overcrowding calls for real solutions. CADTH is examining the evidence and expert-informed considerations and producing a series of publications about the causes and consequences of, and solutions to, ED overcrowding in health care systems across the country. The interventions identified in this report may be of interest to senior health care decision-makers who are anticipating health system innovation and transformation.

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“…Fifty-four models are developed and compared and found to show great promise in predicting patient needs, possibly alleviating crowding issues. 65…”

Section: Machine Learning To Predict Patient Admission Statusmentioning

confidence: 99%

Interventions Intended to Alleviate Emergency Department Overcrowding

Mason,

Secord,

MacDougall

2023

cjht

View full text Add to dashboard Cite

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“…In response to this problem, it is recommended to use the grid search (GS) method [43] to optimize the machine learning method and improve the modeling and estimation results. In many fields, such as medicine [44,45], chemical substances [46], materials [47], finance [48], etc., GS optimized machine learning algorithms have achieved better results, saving time and money, reducing model estimation errors, and significantly improving model estimation accuracy in soil field modeling [49].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests

et al. 2022

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Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

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A study of “left against medical advice” emergency department patients: an optimized explainable artificial intelligence framework

Ahmed,

Aram,

Tutun

et al. 2024

Health Care Manag Sci

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The issue of left against medical advice (LAMA) patients is common in today’s emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause patients to “leave against medical advice” is vital to mitigate and potentially eliminate these adverse outcomes. This paper proposes a framework for studying the factors that affect LAMA in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization-one of the main challenges of machine learning model development. Adaptive tabu simulated annealing (ATSA) metaheuristic algorithm is utilized for optimizing the parameters of extreme gradient boosting (XGB). The optimized XGB models are used to predict the LAMA outcomes for patients under treatment in ED. The designed algorithms are trained and tested using four data groups which are created using feature selection. The model with the best predictive performance is then interpreted using the SHaply Additive exPlanations (SHAP) method. The results show that best model has an area under the curve (AUC) and sensitivity of 76% and 82%, respectively. The best model was explained using SHAP method.

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An integrated optimization and machine learning approach to predict the admission status of emergency patients

Cited by 12 publications

References 69 publications

Interventions Intended to Alleviate Emergency Department Overcrowding

Interventions Intended to Alleviate Emergency Department Overcrowding

Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests

A study of “left against medical advice” emergency department patients: an optimized explainable artificial intelligence framework

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