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Publicações

Publicações por Maria José Pedroto

2019

Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction

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

Publicação
PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

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.

2018

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

Autores
Pedroto, M; Jorge, A; Moreira, JM; Coelho, T;

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

Abstract
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.

2022

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

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

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract
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.

2023

Geovisualisation Tools for Reporting and Monitoring Transthyretin-Associated Familial Amyloid Polyneuropathy Disease

Autores
Lopo, RX; Jorge, AM; Pedroto, M;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.

2023

Clinical model for Hereditary Transthyretin Amyloidosis age of onset prediction

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

Publicação
FRONTIERS IN NEUROLOGY

Abstract
IntroductionHereditary transthyretin amyloidosis (ATTRv amyloidosis) is a rare neurological hereditary disease clinically characterized as severe, progressive, and life-threatening while the age of onset represents the moment in time when the first symptoms are felt. In this study, we present and discuss our results on the study, development, and evaluation of an approach that allows for time-to-event prediction of the age of onset, while focusing on genealogical feature construction. Materials and methodsThis research was triggered by the need to answer the medical problem of when will an asymptomatic ATTRv patient show symptoms of the disease. To do so, we defined and studied the impact of 77 features (ranging from demographic and genealogical to familial disease history) we studied and compared a pool of prediction algorithms, namely, linear regression (LR), elastic net (EN), lasso (LA), ridge (RI), support vector machines (SV), decision tree (DT), random forest (RF), and XGboost (XG), both in a classification as well as a regression setting; we assembled a baseline (BL) which corresponds to the current medical knowledge of the disease; we studied the problem of predicting the age of onset of ATTRv patients; we assessed the viability of predicting age of onset on short term horizons, with a classification framing, on localized sets of patients (currently symptomatic and asymptomatic carriers, with and without genealogical information); and we compared the results with an out-of-bag evaluation set and assembled in a different time-frame than the original data in order to account for data leakage. ResultsCurrently, we observe that our approach outperforms the BL model, which follows a set of clinical heuristics and represents current medical practice. Overall, our results show the supremacy of SV and XG for both the prediction tasks although impacted by data characteristics, namely, the existence of missing values, complex data, and small-sized available inputs. DiscussionWith this study, we defined a predictive model approach capable to be well-understood by medical professionals, compared with the current practice, namely, the baseline approach (BL), and successfully showed the improvement achieved to the current medical knowledge.

2023

Combining Neighbor Models to Improve Predictions of Age of Onset of ATTRv Carriers

Autores
Pedroto, M; Jorge, A; Mendes-Moreira, J; Coelho, T;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT II

Abstract
Transthyretin (TTR)-related familial amyloid polyneuropathy (ATTRv) is a life-threatening autosomal dominant disease and the age of onset represents the moment when first symptoms are felt. Accurately predicting the age of onset for a given patient is relevant for risk assessment and treatment management. In this work, we evaluate the impact of combining prediction models obtained from neighboring time windows on prediction error. We propose Symmetric (Sym) and Asymmetric (Asym) models which represent two different averaging approaches. These are incorporated with a weighting mechanism as to create Symmetric (Sym), Symmetric-weighted (Sym-w), Asymmetric (Asym), and Asymmetric-weighted (Asym-w). These four ensemble models are then compared to the original approach which is focused on individual regression base learners namely: Baseline (BL), Decision Tree (DT), Elastic Net (EN), Lasso (LA), Linear Regression (LR), Random Forest (RF), Ridge (RI), Support Vector Regressor (SV) and XGBoost (XG). Our results show that by aggregating predictions from neighbor models the average mean absolute error obtained by each base learner decreases. Overall, the best results are achieved by regression-based ensemble tree models as base learners.

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