Predicting Hospital Length of Stay for Accident and Emergency Admissions

Kieran Stone, Reyer Zwiggelaar, Phil Jones, Neil MacParthaláin

Research output: Contribution to conferencePaperpeer-review

3 Citations (SciVal)


The primary objective of hospital managers is to establish appropriate healthcare planning and organisation by allocating facilities, equipment and manpower resources necessary for hospital operation in accordance with a patients needs while minimising the cost of healthcare. Length of stay (LoS) prediction is generally regarded as an important measure of inpatient hospitalisation costs and resource utilisation. LoS prediction is critical to ensuring that patients receive the best possible level of care during their stay in hospital. A novel approach for the prediction of LoS is investigated in this paper using only data based upon generic patient diagnoses. This data has been collected during a patients stay in hospital along with other general personal information such as age, sex, etc. A number of different classifiers are employed in order to gain an understanding of the ability to perform knowledge discovery on this limited dataset. They demonstrate a classification accuracy of around 75%. In addition, a further set of perspectives are explored that offer a unique insight into the contribution of the individual features and how the conclusions might be used to influence decision-making, staff and resource scheduling and management
Original languageEnglish
Number of pages13
Publication statusPublished - 30 Aug 2019
EventUK Workshop on Computational Intelligence - University of Portsmouth, Portsmouth, United Kingdom of Great Britain and Northern Ireland
Duration: 04 Sept 201905 Sept 2019
Conference number: 19


WorkshopUK Workshop on Computational Intelligence
Abbreviated titleUKCI 2019
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
Period04 Sept 201905 Sept 2019
Internet address


  • data mining
  • classification
  • medical informatics
  • hospital length of stay
  • decision support
  • Classification
  • Hospital length of stay
  • Data mining
  • Decision support
  • Medical informatics


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