Publicação
A data mining study on pressure ulcers
| Resumo: | Nurses follow well-defined guidelines in order to avoid the occurrence of pressure ulcers (pU) in patients under their care, not being always successful. This work intends to produce prediction models using Data Mining (DM) techniques in order to anticipate uP treatment. The work was conducted in the Oporto Hospital Center (CHP). For the construction of this DM study, the phases of the CRISP DM methodology were taken into account. In particular, the DM focus is to show that the time factor and frequency of interventions may influence the prediction of pU classification models. To prove this, we used a data set (containing 1339 records) where different classification techniques were applied using WEKA tool. Through the classification technique (decision tree), it was possible to create a guideline that contains all the scenarios and instructions that the professional can use in order to avoid patients to develop pU. For its construction we used the model that presented a higher percentage of sensitivity (number of positive cases correctly classified as "NO" developed pU). The conclusions were: the factors studied are good predictors of PU and the guideline obtained, through automatic techniques, can help professionals apply care to the patient more quickly. |
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| Autores principais: | Mota, Francisco |
| Outros Autores: | Abreu, Nuno; Guimarães, Tiago André Saraiva; Santos, Manuel |
| Assunto: | Data Mining Classification CRISP-DM WEKA Pressure Ulcers |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Nurses follow well-defined guidelines in order to avoid the occurrence of pressure ulcers (pU) in patients under their care, not being always successful. This work intends to produce prediction models using Data Mining (DM) techniques in order to anticipate uP treatment. The work was conducted in the Oporto Hospital Center (CHP). For the construction of this DM study, the phases of the CRISP DM methodology were taken into account. In particular, the DM focus is to show that the time factor and frequency of interventions may influence the prediction of pU classification models. To prove this, we used a data set (containing 1339 records) where different classification techniques were applied using WEKA tool. Through the classification technique (decision tree), it was possible to create a guideline that contains all the scenarios and instructions that the professional can use in order to avoid patients to develop pU. For its construction we used the model that presented a higher percentage of sensitivity (number of positive cases correctly classified as "NO" developed pU). The conclusions were: the factors studied are good predictors of PU and the guideline obtained, through automatic techniques, can help professionals apply care to the patient more quickly. |
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