Publicação
Deep Learning for predicting disease progression of clinical endpoints in ALS
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | pt_PT |
| dc.contributor.advisor | Madeira, Sara Alexandra Cordeiro | |
| dc.contributor.advisor | Garcia, Nuno Ricardo da Cruz | |
| dc.contributor.author | Silva, Lucas Barreto | |
| dc.date.accessioned | 2023-05-23T17:07:40Z | |
| dc.date.available | 2023-05-23T17:07:40Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2022 | |
| dc.description | Tese de Mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease vastly known for its rapid progression, usually leading to death within a few years, by respiratory failure. Since there is no cure currently known, the main objective is treatment in order to improve symptoms and prolong survival. A treatment that is known to be effective, is Non-invasive Ventilation (NIV), being capable of extending life expectancy and improving quality of life. Therefore it is imperative to administrate it preemptively. However Amyotrophic Lateral Sclerosis (ALS) affects most muscles of the body, so there will exist many clinical conditions that require some kind of treatment. In this work, we propose two approaches that use deep learning to predict Amyotrophic Lateral Sclerosis (ALS) disease progression, to know when a patient should be treated or not for a given clinical endpoint. In the first approach, we use the various snapshots of the patients as input without taking into consideration the temporal dependence between the available features, so instead we use each assessment of the patient as a single instance to feed the models. In this approach, we propose the use of a MLP and a CNN, to predict the outcome for the time windows available (90, 180 and 365 days), using the instances mentioned before and perform class resampling on the training sets, using SMOTE on the minority class and Random Undersampling on the majority class. In order to have more data to train the models and have a balanced set, which helps achieving a better performing model on the test set. In the second approach, we take the snapshots of the patients and group them by the patient reference, and proceed to only used those that have length of 3 and 4. On the sequences of length 3, we performed padding in order to have the same length as the sequences of length 4, by simply taking the first instance of this sequence and use it as the first and second assessment on the sequences. We proceed to use the sequences to feed them into a LSTM model, and train for the several datasets and retrieve the scores obtained. On this approach we do not perform any type of class resampling, as the first approach does. The results obtained show some promising insights, on the first approach in which we use the instances of the patients, the preprocessing performed was one of the main factors to the great results obtained. On the second approach, which the temporal sequences of length 3 and 4 are used, the results obtained are not as promissing as the first approach, however there are still improvements that could be done. | pt_PT |
| dc.identifier.tid | 203499590 | |
| dc.identifier.uri | http://hdl.handle.net/10451/57551 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | PTDC/CCICIF/4613/2020 | pt_PT |
| dc.subject | ALS Functional Rating Scale | pt_PT |
| dc.subject | Data Imputation | pt_PT |
| dc.subject | Deep Learning | pt_PT |
| dc.subject | Esclerose Lateral Amiotrófica | pt_PT |
| dc.subject | Ventilação não-invasiva | pt_PT |
| dc.subject | Teses de mestrado - 2022 | pt_PT |
| dc.title | Deep Learning for predicting disease progression of clinical endpoints in ALS | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Engenharia Informática | pt_PT |