Abstract
Knowing a patient’s risk at the moment of admission to a medical unit is important for both clinical and administrative decision making: it is fundamental to carry out a health technology assessment. In this paper, we propose a non-supervised learning method based on cluster analysis and genetic algorithms to classify patients according to their admission risk. This proposal includes an innovative way to incorporate the information contained in the diagnostic hypotheses into the classification system. To assess this method, we used retrospective data of 294 patients (50 dead) admitted to two Adult Intensive Care Units (ICU) in the city of Santiago, Chile. An area calculation under the ROC curve was used to verify the accuracy of this classification. The results show that, with the proposed methodology, it is possible to obtain an ROC curve with a 0.946 area, whereas with the APACHE II system it is possible to obtain only a 0.786 area.
This study has been supported by FONDECYT (Chile) project No. 1990920 and DICYT-USACH No. 02-0219-01CHP.
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Keywords
- Genetic Algorithm
- Health Technology Assessment
- Adult Intensive Care Unit
- Diagnostic Hypothesis
- Genetic Algorithm Grouping
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Chacón, M., Luci, O. (2003). Patients Classification by Risk Using Cluster Analysis and Genetic Algorithms. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds) Progress in Pattern Recognition, Speech and Image Analysis. CIARP 2003. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24586-5_43
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DOI: https://doi.org/10.1007/978-3-540-24586-5_43
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