HEALTH OUTCOME PATHWAY PREDICTION. A GRAPH-BASED FRAMEWORK

• Autor(a) principal: Cardoso, Miguel Simão Dórdio
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/151158
• topic: SNS24; Frailty; Health Outcomes; Diagnosis Prediction; Few-shot Learning; Graph Neural Networks; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

This dissertation is part of the project FrailCare.AI, which aims to detect frailty in the elderly Portuguese population in order to optimize the SNS24 (telemonitoring) service, with the goal of suggesting health pathways to reduce the patients frailty. Frailty can be defined as the condition of being weak and delicate which normally increases with age and is the consequence of several health and non-health related factors. A patient health journey is recorded in Eletronic Health Record (EHR), which are rich but sparse, noisy and multi-modal sources of truth. These can be used to train predictive models to predict future health states, where frailty is just one of them. In this work, due to lack of data access we pivoted our focus to phenotype prediction, that is, predicting diagnosis. What is more, we tackle the problem of data-insufficiency and class imbalance (e.g. rare diseases and other infrequent occurrences in the training data) by integrating standardized healthcare ontologies within graph neural networks. We study the broad task of phenotype prediction, multi-task scenarios and as well few-shot scenarios - which is when a class rarely occurs in the training set. Furthermore, during the development of this work we detect some reproducibility issues in related literature which we detail, and also open-source all of our implementations introduding a framework to aid the development of similar systems.