Forecasting and explaining emergency department visits in a public hospital

Petsis, Spyridon; Karamanou, Areti; Kalampokis, Evangelos; Tarabanis, Konstantinos

doi:10.1007/s10844-022-00716-6

Cited by 15 publications

(10 citation statements)

References 52 publications

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“…All in all, the study used 712 explanatory variables for forecasting and concluded that XGBoost was best performing in their setting. Meanwhile, another recent study bearing parallels with our research was conducted by Petsis et al [ 37 ], who focused on predicting daily patient flows 1 and 2 time periods ahead. This work is reported to be the first study that has started to incorporate XAI techniques into illuminating the behaviour of the underlying models.…”

Section: Related Workmentioning

confidence: 70%

“…The features used for the forecasting models in the prior works have chiefly relied on autoregressive (previous or lagged values) and various calendar variables [ 5 , 26 , 48 ] with some studies using variables indicating school holidays as well. There has been a recent trend to also incorporate weather-related variables [ 7 , 37 , 42 , 53 ], while air quality and pollution information have been a subject of some studies [ 32 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting patient flows with pandemic induced concept drift using explainable machine learning

Sušnjak

Maddigan

2023

EPJ Data Sci.

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Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns.This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions. This research also uniquely contributes to the body of work in this domain by employing tools from the eXplainable AI field to investigate more deeply the internal mechanics of the models than has previously been done.The Voting ensemble-based method combining machine learning and statistical techniques was the most reliable in our experiments. Our study showed that the prevailing COVID-19 Alert Level feature together with Google search terms and pedestrian traffic were effective at producing generalisable forecasts. The implications of this study are that proxy variables can effectively augment standard autoregressive features to ensure accurate forecasting of patient flows. The experiments showed that the proposed features are potentially effective model inputs for preserving forecast accuracies in the event of future pandemic outbreaks.

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Section: Related Workmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Forecasting patient flows with pandemic induced concept drift using explainable machine learning

Sušnjak

Maddigan

2023

EPJ Data Sci.

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“…In Appendix Table A3, algorithms are grouped by similarity and summarized, with primary references provided for readers to access detailed information. The selection of these algorithms was motivated by the following reasons: (i) XGBoost [37], [45], RF [7], [21], and GLMNET [21] are reported as presenting superior performance in patient arrival prediction; (ii) to the best of our knowledge, LightGBM, SVM-RBF, and NNAR are being used for the first time in such context; (iii) XGBoost, LightGBM, and GLMNET are the fastest machine learning methods in terms of execution speed and computational efficiency [73]- [75].…”

Section: Overview Of Selected Machine Learning Algorithms and Perform...mentioning

confidence: 99%

“…Reference source not found. used some form of cross-validation to assess the quality of the predictions, with five studies [35]- [37] conducting cross-validation only on the training set and three studies [3], [7], [21] on the complete datasets. Cross-validation provides a more robust and reliable way to measure model performance [50].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms

Porto,

Fogliatto

2024

Preprint

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Background Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of patient arrivals in EDs can serve as a management baseline for better allocation of staff and medical resources. In this article, we investigate the use of calendar and meteorological predictors, as well as feature engineered variables, to forecast daily patient arrivals using datasets from eleven different EDs across 3 countries. Methods Six machine learning algorithms were tested, considering forecasting horizons of 7 and 45 days ahead. Tuning of hyperparameters was performed using a grid-search with cross-validation. Algorithms' performance was evaluated using 5-fold cross-validation and four performance metrics. Results The eXtreme Gradient Boosting (XGBoost) achieved better performance considering the two prediction horizons compared to other models, also outperforming results reported in past studies on ED arrival prediction. This is also the first study to utilize Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF) and Neural Network Autoregression (NNAR) for predicting patient arrivals at EDs. Conclusion The Random Forest (RF) variable selection and grid-search methods improved the accuracy of the algorithms tested. Our study innovates by using feature engineering to predict patient arrivals in EDs.

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Artificial Intelligence and Blockchain Technologies in the Public Sector: A Research Projects Perspective

Kalampokis

Karacapilidis²,

Tsakalidis³

et al. 2022

Lecture Notes in Computer Science

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Forecasting and explaining emergency department visits in a public hospital

Cited by 15 publications

References 52 publications

Forecasting patient flows with pandemic induced concept drift using explainable machine learning

Forecasting patient flows with pandemic induced concept drift using explainable machine learning

Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms

Artificial Intelligence and Blockchain Technologies in the Public Sector: A Research Projects Perspective

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