Improving the Prediction of Age of Onset of TTR-FAP Patients Using Graph-Embedding Features

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

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

Conference proceedings International

Title: Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31 - September 2, 2022, Proceedings

Pages: 183-194

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00X-4SK

DOI: 10.1007/978-3-031-16474-3_16

Abstract (EN): Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic illness that inflicts severe symptoms after the onset occurs. Age of onset represents the moment a patient starts to experience the symptoms of a disease. An accurate prediction of this event can improve clinical and operational guidelines that define the work of doctors, nurses, and operational staff. In this work, we transform family trees into compact vectors, that is, embeddings, and handle these as input features to predict the age of onset of patients with TTR-FAP. Our purpose is to evaluate how information present in genealogical trees can be transformed and used to improve a regression-based setting for TTR-FAP age of onset prediction. Our results show that by combining manual and graph-embeddings features there is a decrease in the mean prediction error when there is less information regarding a patient's family. With this work, we open the way for future work in representation learning for genealogical data, enabling a more effective exploitation of machine learning approaches.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12