Publications

Publications by LIAAD

2019

Impact of genealogical features in transthyretin familial amyloid polyneuropathy age of onset prediction

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
Advances in Intelligent Systems and Computing

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient’s attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features. © Springer Nature Switzerland AG 2019.

CloseRead Abstract

2019

The 2nd International Workshop on Narrative Extraction from Text: Text2Story 2019

Authors
Jorge, AM; Campos, R; Jatowt, A; Bhatia, S;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2019

Guest Editorial: Special Issue on Data Mining for Geosciences

Authors
Jorge, A; Lopes, RL; Larrazabal, G; Nikhalat Jahromi, H;

Publication
Data Mining and Knowledge Discovery

Abstract

2019

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Authors
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;

Publication
Journal of Medical Systems

Abstract

2019

Guest Editorial

Authors
Jorge, A; Lopes, RL; Larrazabal, G; Nikhalat-Jahromi, H;

Publication
Data Mining and Knowledge Discovery

Abstract

2019

Interactive system for automatically generating temporal narratives

Authors
Pasquali, A; Mangaravite, V; Campos, R; Jorge, AM; Jatowt, A;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
In this demo, we present a tool that allows to automatically generate temporal summarization of news collections. Conta-me Histórias (Tell me stories) is a friendly user interface that enables users to explore and revisit events in the past. To select relevant stories and temporal periods, we rely on a key-phrase extraction algorithm developed by our research team, and event detection methods made available by the research community. Additionally, we offer the engine as an open source package that can be extended to support different datasets or languages. The work described here stems from our participation at the Arquivo.pt 2018 competition, where we have been awarded the first prize. © Springer Nature Switzerland AG 2019.

CloseRead Abstract