Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference.

Akkoyun, Emrah; Kwon, Sebastian T; Acar, Aybar C; Lee, Whal; Baek, Seungik

Akkoyun, Emrah; Kwon, Sebastian T; Acar, Aybar C; Lee, Whal; Baek, Seungik.

Afiliação

Akkoyun E; Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Dumlupinar Bulvari #1, 06800, Cankaya, Ankara, Turkey.
Kwon ST; Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, 757 Westwood Blvd., Los Angeles, CA, 90095, USA.
Acar AC; Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Dumlupinar Bulvari #1, 06800, Cankaya, Ankara, Turkey.
Lee W; Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, Republic of Korea.
Baek S; Department of Mechanical Engineering, Michigan State University, 2457 Engineering Building, East Lansing, MI, 48824, USA. Electronic address: sbaek@egr.msu.edu.

Comput Biol Med ; 117: 103620, 2020 02.

Article em En | MEDLINE | ID: mdl-32072970

Assuntos

Aneurisma da Aorta Abdominal; Aneurisma da Aorta Abdominal/diagnóstico por imagem; Teorema de Bayes; Humanos; Estudos Retrospectivos; Fatores de Risco; Fatores de Tempo; Tomografia Computadorizada por Raios X

Palavras-chave

Abdominal aortic aneurysm; Bayesian inference; Patient-oriented prediction model; Probabilistic programming; Rupture risk assessment

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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