Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W.

Affiliation

Stylianou N; Centre for Health Informatics, Institute of Population Health, University of Manchester, UK. Electronic address: neophytos.stylianou@postgrad.manchester.ac.uk.
Akbarov A; Centre for Health Informatics, Institute of Population Health, University of Manchester, UK.
Kontopantelis E; Centre for Health Informatics, Institute of Population Health, University of Manchester, UK.
Buchan I; Centre for Health Informatics, Institute of Population Health, University of Manchester, UK.
Dunn KW; University Hospital South Manchester, Greater Manchester, UK.

Burns ; 41(5): 925-34, 2015 Aug.

Article in En | MEDLINE | ID: mdl-25931158

Subject(s)

Burns/mortality; Decision Support Techniques; Machine Learning; Models, Statistical; Registries; Smoke Inhalation Injury/mortality; Adolescent; Adult; Age Factors; Bayes Theorem; Body Surface Area; Burns/pathology; Child; Child, Preschool; England; Female; Humans; Infant; Logistic Models; Male; Neural Networks, Computer; ROC Curve; Risk Assessment/methods; Software; Support Vector Machine; Wales; Young Adult

Key words

Burn; Clinical prediction; Machine learning; Mortality

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Burns / Smoke Inhalation Injury / Registries / Models, Statistical / Decision Support Techniques / Machine Learning Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Adolescent / Adult / Child / Child, preschool / Female / Humans / Infant / Male Country/Region as subject: Europa Language: En Journal: Burns Journal subject: TRAUMATOLOGIA Year: 2015 Type: Article

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