Multiple Organ Failure Diagnosis Using Adverse Events and Neural Networks
In the past years, the Clinical Data Mining arena has suffered a remarkable development, where intelligent data analysis tools, such as Neural Networks, have been successfully applied in the design of medical systems. In this work, Neural Networks are applied to the prediction of organ dysfunction i...
| Autor principal: | |
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| Outros Autores: | , , , |
| Formato: | bookPart |
| Idioma: | eng |
| Publicado em: |
2006
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/5918 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/5918 |
| Resumo: | In the past years, the Clinical Data Mining arena has suffered a remarkable development, where intelligent data analysis tools, such as Neural Networks, have been successfully applied in the design of medical systems. In this work, Neural Networks are applied to the prediction of organ dysfunction in Intensive Care Units. The novelty of this approach comes from the use of adverse events, which are triggered from four bedside alarms, being achieved an overall predictive accuracy of 70%. |
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