Abstract
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 language | English |
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Pages | 283-295 |
Number of pages | 13 |
DOIs | |
Publication status | Published - 30 Aug 2019 |
Event | UK Workshop on Computational Intelligence - University of Portsmouth, Portsmouth, United Kingdom of Great Britain and Northern Ireland Duration: 04 Sept 2019 → 05 Sept 2019 Conference number: 19 https://www.ukci2019.port.ac.uk/ |
Workshop
Workshop | UK Workshop on Computational Intelligence |
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Abbreviated title | UKCI 2019 |
Country/Territory | United Kingdom of Great Britain and Northern Ireland |
City | Portsmouth |
Period | 04 Sept 2019 → 05 Sept 2019 |
Internet address |
Keywords
- data mining
- classification
- medical informatics
- hospital length of stay
- decision support
- Classification
- Hospital length of stay
- Data mining
- Decision support
- Medical informatics