Publicação
Relapse Prediction in Multiple Sclerosis: a Supervised Learning Approach
| datacite.subject.fos | Departamento de Informática | pt_PT |
| dc.contributor.advisor | Tomás, Helena Isabel Aidos Lopes | |
| dc.contributor.advisor | Madeira, Sara Alexandra Cordeiro | |
| dc.contributor.author | Valente, Joana Filipa Barros | |
| dc.date.accessioned | 2023-03-22T11:43:29Z | |
| dc.date.available | 2024-11-30T01:30:41Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2022 | |
| dc.description | Tese de mestrado, Ciência de Dados, 2022, Universidade de Lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting, approximately, 2.8 million people around the world. Its onset may be abrupt or insidious and its evolution, clinical manifestation, and treatment response may vary widely across patients, making it difficult to predict and manage the disease outcomes. The majority of MS patients (around 85%-90%) are diagnosed with relapse-remitting MS (RRMS), a form of the disease that is characterised by alternating periods of relapses and of total or partial recovery (remission). These relapses last at least 24h, develop acutely or subacutely, are mostly monosymptomatic, and are documented to affect patients’ disability levels, at least in the early stages of the disease, and their social, working, and household activities. Despite all the existing research focused on understanding the characteristics and impact of relapses, these remain largely unpredictable both in time and location. Moreover, the fact that this disease manifestation affects most MS patients and that the anticipation of a future relapse may help clinicians to provide more timely and effective treatments to MS patients, emphasizes the need to develop models that are able to predict when a next relapse may occur. Using a dataset containing 3,679 relapses from 859 patients, this study proposes developing a prediction model for when a future relapse may occur based on the clinical profile of RRMS patients at the time of relapse. To this end, three classifiers were learnt (Logistic Regression, Decision Tree, and Random Forest), considering as target variables if a relapse happened within one or two years, and as a clinical profile the combination of demographic, symptoms, disability, treatment, and MRI data. The best-performing model was the Random Forest, with an AUC of 68%, when evaluating if a relapse may happen within two years from the previous relapse. The most relevant variables for predicting the timing of a future relapse include the time elapsed between the two most recent relapses, the number of relapses already endured by a patient, the number of different medicines the patient has tried since the onset of the disease, disease duration, the number of symptoms experienced in a relapse, and the age at onset. Overall, the findings reported in this study can help clinicians to assess the future prospects of the disease, for a given patient, and to tailor the healthcare provided accordingly. | pt_PT |
| dc.identifier.tid | 203490290 | |
| dc.identifier.uri | http://hdl.handle.net/10451/56753 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Esclerose Múltipla | pt_PT |
| dc.subject | Esclerose Múltipla Recidivante Remitente | pt_PT |
| dc.subject | Surtos | pt_PT |
| dc.subject | Aprendizagem Automática | pt_PT |
| dc.subject | Análise preditiva | pt_PT |
| dc.subject | Teses de mestrado - 2023 | pt_PT |
| dc.title | Relapse Prediction in Multiple Sclerosis: a Supervised Learning Approach | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Tese de mestrado em Ciência de Dados | pt_PT |