Skip to main content

Advertisement

Log in

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study

Internal and Emergency Medicine Aims and scope Submit manuscript

Abstract

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

References

  1. Ou L, Chen J, Young L, Santiano N, Baramy L, Hillman K (2011) Effective discharge planning—timely assignment of an estimated date of discharge. Aust Health Rev 35(3):357–363

    Article  Google Scholar 

  2. Bacchi S, Gluck S, Tan Y, Chim I, Cheng J, Gilbert T, Menon DK, Jannes J, Kleinig T, Koblar S (2020) Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med. https://doi.org/10.1007/s11739-019-02265-3

    Article  PubMed  Google Scholar 

  3. Huang Z, Juarez JM, Duan H, Li H (2013) Length of stay prediction for clinical treatment process using temporal similarity. Expert Syst Appl 40(16):6330–6339. https://doi.org/10.1016/j.eswa.2013.05.066

    Article  Google Scholar 

  4. Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35

    Article  CAS  Google Scholar 

  5. Hand DJ (2012) Assessing the performance of classification methods. Int Stat Rev 80(3):400–414. https://doi.org/10.1111/j.1751-5823.2012.00183.x

    Article  Google Scholar 

  6. Falavigna G, Costantino G, Furlan R, Quinn JV, Ungar A, Ippoliti R (2018) Artificial neural networks and risk stratification in emergency departments. Intern Emerg Med 14(2):291–299. https://doi.org/10.1007/s11739-018-1971-2

    Article  PubMed  Google Scholar 

  7. Obermeyer Z, Emanuel E (2016) Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216–1219

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephen Bacchi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of human and animal rights

Ethics approval was granted for this project by the Central Adelaide Local Health Network Research Ethics Committee (HREC/19/CALHN/209).

Informed consent

For this type of study, a waiver of consent was granted, and consent was not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bacchi, S., Gluck, S., Tan, Y. et al. Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study. Intern Emerg Med 16, 1613–1617 (2021). https://doi.org/10.1007/s11739-021-02697-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11739-021-02697-w

Keywords

Navigation