Predict Hourly Patient Discharge Probability in Intensive Care Units using Data Mining

Portela Filipe; Veloso Rui; Oliveira Sergio; Santos Manuel Filipe; Abelha Antonio; Machado Jose; Silva Alvaro  and Rua Fernando  nbsp

doi:10.17485/ijst/2015/v8i32/92043

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Predict Hourly Patient Discharge Probability in Intensive Care Units using Data Mining

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2015/v8i32/92043

Year: 2015, Volume: 8, Issue: 32, Pages: 1-11

Original Article

Predict Hourly Patient Discharge Probability in Intensive Care Units using Data Mining

Portela Filipe^1*, Veloso Rui¹ , Oliveira Sergio¹ , Santos Manuel Filipe¹ , Abelha Antonio¹ , Machado Jose¹, Silva Alvaro² and Rua Fernando²

¹Algoritmi Research Centre, University of Minho, Guimaraes; Information System Department, University of Minho, 4800-058, Guimaraes, Portugal; [email protected]
² Intensive Care Unit, Centro Hospitalar do Porto, Porto

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The length of stay (LOS) is an important metric to manage hospital units since a correct prevision of the LOS can contribute to reduce costs and optimize resources. This metric become more fundamental in intensive care units (ICU) where controlling patient condition and predict clinical events is very difficult. A set of experiences was made using data mining techniques in order to predict something more ambitious than LOS. Using the data provided by INTCare system it was possible to induce models with a very good sensitivity (95%) in order to predict the probability of a patient be discharged in the next hour. The results achieved also allow for predicting the bed occupancyrate in ICU for the next hour. The work done represents a novelty in this area and contributes to improve the decision making process providing new knowledge in real time.
Keywords: Data Mining, ICU, INTCare, LOS, Occupancy Rate