Conference Publication Details
Mandatory Fields
Mahmoud Elbattah, Owen Molloy
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016
Using Machine Learning to Predict Length of Stay and Discharge Destination for Hip-Fracture Patients
2017
August
Published
1
Optional Fields
Yaxin Bi,‎ Supriya Kapoor,‎ Rahul Bhatia
207
217
London, UK
21-SEP-16
22-SEP-16
Faced with the challenge of population ageing, healthcare providers are increasingly in need for evidence-based artefacts to support the decision making process. In this regard, the paper avails of machine learning techniques in a bid to support the elderly care planning with application to hip fracture care in Ireland. Specifically, the inpatient length of stay (LOS), and discharge destination are aimed to be predicted based on learning from patient historical data. The accuracy of various regression and classification techniques was investigated. Random Forests proved to provide a considerable higher accuracy in comparison to other algorithms in our case. The prediction models were trained using the Azure Machine Learning Studio. Furthermore, the models were published as predictive web services on top of the Azure cloud platform. The developed predictors are claimed to make predictions on the LOS and discharge destinations with high accuracy.
https://link.springer.com/chapter/10.1007/978-3-319-56994-9_15
https://doi.org/10.1007/978-3-319-56994-9_15
Grant Details
Publication Themes
Informatics, Physical and Computational Sciences