Predicting Age of Onset in TTR-FAP Patients with Genealogical Features

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Maria Pedroto (Autor) Outra; Jorge, AM (Autor) FCUP; João Mendes-Moreira (Autor) FEUP; Coelho, T (Autor) Outra

Ata de Conferência Internacional

Título: 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, June 18-21, 2018

Páginas: 199-204

31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018

18 June 2018 through 21 June 2018

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00P-25R

DOI: 10.1109/CBMS.2018.00042

Abstract (EN): This work describes a problem oriented approach to analyze and predict the Age of Onset of Patients diagnosed with Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). We constructed, from a set of clinical and familial records, three sets of features which represent different characteristics of a patient, before becoming symptomatic. Using those features, we tested a set of machine learning regression methods, namely Decision Tree (Regression Tree), Elastic Net, Lasso, Linear Regression, Random Forest Regressor, Ridge Regression and Support Vector Machine Regressor (SVM). Later, we defined a baseline model that represents the current medical practice to serve as a guideline for us to measure the accuracy of our approach. Our results show a significant improvement of machine learning methods when compared with the current baseline. © 2018 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica