Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding (2207.00610v3)

Published 1 Jul 2022 in cs.CY and cs.LG

Abstract: Emergency Departments (EDs) are a fundamental element of the Portuguese National Health Service, serving as an entry point for users with diverse and very serious medical problems. Due to the inherent characteristics of the ED; forecasting the number of patients using the services is particularly challenging. And a mismatch between the affluence and the number of medical professionals can lead to a decrease in the quality of the services provided and create problems that have repercussions for the entire hospital, with the requisition of health care workers from other departments and the postponement of surgeries. ED overcrowding is driven, in part, by non-urgent patients, that resort to emergency services despite not having a medical emergency and which represent almost half of the total number of daily patients. This paper describes a novel deep learning architecture, the Temporal Fusion Transformer, that uses calendar and time-series covariates to forecast prediction intervals and point predictions for a 4 week period. We have concluded that patient volume can be forecasted with a Mean Absolute Percentage Error (MAPE) of 5.90% for Portugal's Health Regional Areas (HRA) and a Root Mean Squared Error (RMSE) of 84.4102 people/day. The paper shows empirical evidence supporting the use of a multivariate approach with static and time-series covariates while surpassing other models commonly found in the literature.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in western australia. Journal of Biomedical Informatics, 57:62–73, 2015. ISSN 1532-0464. https://doi.org/10.1016/j.jbi.2015.06.022.
  2. Forecasting the emergency department patients flow. Journal of Medical Systems, 40(7):175, 2016. ISSN 1573-689X. 10.1007/s10916-016-0527-0.
  3. Predicting patient visits to an urgent care clinic using calendar variables. Academic Emergency Medicine, 8(1):48–53, 2001. 10.1111/j.1553-2712.2001.tb00550.x.
  4. The effect of emergency department crowding on clinically oriented outcomes. Academic Emergency Medicine, 16(1):1–10, 2009. https://doi.org/10.1111/j.1553-2712.2008.00295.x.
  5. Predicting emergency department admissions. Emergency Medicine Journal, 29(5):358–365, 2012. ISSN 1472-0205. 10.1136/emj.2010.103531.
  6. Assessment of forecasting models for patients arrival at emergency department. Operations Research for Health Care, 18:112–118, 2018. ISSN 2211-6923. https://doi.org/10.1016/j.orhc.2017.05.001.
  7. Forecasting emergency department presentations. Australian Health Review, 31(1):83–90, 2007. 10.1071/AH070083.
  8. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, 2019. Association for Computational Linguistics. 10.18653/v1/N19-1423.
  9. Use of calendar and weather data to predict walk-in attendance. Southern medical journal, 74(6):709–712, 1981. 10.1097/00007611-198106000-00020.
  10. Forecasting emergency department visits using internet data. Annals of Emergency Medicine, 65(4):436–442.e1, 2015. ISSN 0196-0644. https://doi.org/10.1016/j.annemergmed.2014.10.008.
  11. Can we accurately forecast non-elective bed occupancy and admissions in the nhs? a time-series msarima analysis of longitudinal data from an nhs trust. BMJ Open, 12(4), 2022. ISSN 2044-6055. 10.1136/bmjopen-2021-056523.
  12. Multi-horizon time series forecasting with temporal attention learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’19, page 2527–2535, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450362016. 10.1145/3292500.3330662.
  13. R D Farmer and J Emami. Models for forecasting hospital bed requirements in the acute sector. Journal of Epidemiology & Community Health, 44(4):307–312, 1990. ISSN 0143-005X. 10.1136/jech.44.4.307.
  14. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477):359–378, 2007. 10.1198/016214506000001437.
  15. Forecasting emergency department overcrowding: A deep learning framework. Chaos, Solitons & Fractals, 139:110247, 2020. ISSN 0960-0779. 10.1016/j.chaos.2020.110247.
  16. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016. 10.1109/CVPR.2016.90.
  17. Morten Hertzum. Forecasting hourly patient visits in the emergency department to counteract crowding. The Ergonomics Open Journal, 10(1), 2017. 10.2174/1875934301710010001.
  18. Predicting daily visits to a waik-in clinic and emergency department using calendar and weather data. Journal of General Internal Medicine, 11(4):237–239, 1996.
  19. A flexible simulation platform to quantify and manage emergency department crowding. BMC Medical Informatics and Decision Making, 14(1):50, 2014. ISSN 1472-6947. 10.1186/1472-6947-14-50.
  20. Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine, 15(2):159–170, 2008. 10.1111/j.1553-2712.2007.00032.x.
  21. Rnn-based deep-learning approach to forecasting hospital system demands: application to an emergency department. International Journal of Data Science, 5:1–25, 2020. 10.1504/IJDS.2020.10031621.
  22. Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38(9):107, 2014. ISSN 1573-689X. 10.1007/s10916-014-0107-0.
  23. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, volume 30 of NIPS’17, pages 3149––3157, Red Hook, NY, USA, 2017. Curran Associates, Inc. URL https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf.
  24. Quantile regression. Journal of Economic Perspectives, 15(4):143–156, December 2001. 10.1257/jep.15.4.143.
  25. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4):1748–1764, 2021. ISSN 0169-2070. https://doi.org/10.1016/j.ijforecast.2021.03.012.
  26. Spyros Makridakis. Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4):527–529, 1993. ISSN 0169-2070. https://doi.org/10.1016/0169-2070(93)90079-3.
  27. PC Milner. Forecasting the demand on accident and emergency departments in health districts in the trent region. Statistics in medicine, 7(10):1061–1072, 1988. 10.1002/sim.4780071007.
  28. Comparing arima and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in madrid. Stochastic Environmental Research and Risk Assessment, 32(10):2849–2859, 2018. ISSN 1436-3259. 10.1007/s00477-018-1519-z.
  29. Emergency department crowding is associated with poor care for patients with severe pain. Annals of emergency medicine, 51(1):1–5, 2008. 10.1016/j.annemergmed.2007.07.008.
  30. Eetu Pulkkinen. forecasting emergency department arrivals with neural networks. Bachelor’s thesis, Tampere University, Tampere, Finland, 2020.
  31. Time series analysis of variables associated with daily mean emergency department length of stay. Annals of emergency medicine, 49(3):265–271, 2007. doi:10.1016/j.annemergmed.2006.11.007.
  32. Forecasting emergency department admissions. Journal of Intelligent Information Systems, 56(3):509–528, 2021. ISSN 1573-7675. 10.1007/s10844-021-00638-9.
  33. Forecasting models of emergency department crowding. Academic Emergency Medicine, 16(4):301–308, 2009. https://doi.org/10.1111/j.1553-2712.2009.00356.x.
  34. Patientflownet: A deep learning approach to patient flow prediction in emergency departments. IEEE Access, 9:45552–45561, 2021. 10.1109/ACCESS.2021.3066164.
  35. Performance evaluation of emergency department patient arrivals forecasting models by including meteorological and calendar information: A comparative study. Computers in Biology and Medicine, 135:104541, 2021. ISSN 0010-4825. https://doi.org/10.1016/j.compbiomed.2021.104541.
  36. Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach. BMC Medical Informatics and Decision Making, 22:134, 2022. 10.1186/s12911-022-01878-7.
  37. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
  38. A systematic review of models for forecasting the number of emergency department visits. Emergency Medicine Journal, 26(6):395–399, 2009. ISSN 1472-0205. 10.1136/emj.2008.062380.
  39. Forecasting arrivals and occupancy levels in an emergency department. Operations Research for Health Care, 21:1–18, 2019. ISSN 2211-6923. https://doi.org/10.1016/j.orhc.2019.01.002.
  40. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45. Association for Computational Linguistics, 2020. 10.18653/v1/2020.emnlp-demos.6.
  41. Performance of triage systems in emergency care: a systematic review and meta-analysis. British Medical Journal Open, 9(5), 2019. ISSN 2044-6055. 10.1136/bmjopen-2018-026471.
  42. Time series model for forecasting the number of new admission inpatients. BMC medical informatics and decision making, 18(1):39, 2018. ISSN 1472-6947. 10.1186/s12911-018-0616-8.
Citations (6)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now