Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/30785

TitleReal-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
Author(s)Gonçalves, João
Portela, Filipe
Santos, Manuel Filipe
Silva, Álvaro
Machado, José Manuel
Abelha, António
Rua, Fernando
KeywordsData Mining
Classification
Intensive Care
Sepsis
Predict Therapeutic Plans
Intcare
Classification models
INTCare project
Sepsis level
Therapeutic plans
Issue date6-Nov-2014
PublisherIGI Global
JournalInternational Journal of Healthcare Information Systems and Informatics (IJHISI)
Abstract(s)This work aims to support doctor’s decision-making on predicting sepsis level and the best treatment for patients with microbiological problems. A set of Data Mining (DM) models was developed using forecasting techniques and classification models which will enable doctors’ decisions about the appropriate therapy to apply, as well as the most successful one. The data used in DM models were collected at the Intensive Care Unit (ICU) of the Centro Hospitalar do Porto, in Oporto, Portugal. Classification models where considered to predict sepsis level and therapeutic plan for patients with sepsis in a supervised learning approach. Models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. Confusion Matrix, including associated metrics, and Cross-validation were used for the evaluation. Analysis of the total error rate, sensitivity, specificity and accuracy were the associated metrics used to identify the most relevant measures to predict sepsis level and treatment plan under study. In conclusion, it was possible to predict with great accuracy the sepsis level (2nd and 3rd), but not the therapeutic plan. Although the good results attained for sepsis (accuracy: 100%), therapeutic plan does not present the same level of accuracy (best: 62.8%).
TypeArticle
Description"Accepted for publication"
URIhttp://hdl.handle.net/1822/30785
DOI10.4018/ijhisi.2014070103
ISSN1555-3396
1555-340X
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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