Predicting Clinically Relevant Endpoints in ALS using Multi-label Classification

Oliveira, Joel Pereira de

http://hdl.handle.net/10400.5/99991

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Authors

Oliveira, Joel Pereira de

Advisor(s)

Tomás, Helena Isabel Aidos Lopes

Madeira, Sara

Abstract(s)

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease, characterized by the progressive loss of motor neurons, causing muscular impairment. This disease has no cure, and has a reduced life expectancy of 3-5 years. The common procedure is to perform clinical interventions that aid the patient, being it to either improve their life expectancy or to improve their quality of life. However, this disease has an highly clinical heterogeneity making it a hard task for clinicians to perform decisions. Some relevant clinical interventions performed as the disease progresses are: 1) Non-invasive Ventilation - shown in multiple studies to increase life expectancy; 2) Auxiliary Communication Device - increases the well being of the patients by enabling communication; 3) Percutaneous Endoscopic Gastrostomy - increases life expectancy by providing nutrition; 4) Caregiver - improves quality of life when the disease starts spreading; 5) Wheelchair - improves quality of life by enabling the patient to move after losing the ability to walk. This dissertation presents a study on the evolution of these endpoints, by showing the common progression patterns between them and which onset regions might have an impact on the progression. Then, two prognostic models are trained to predict which of these clinical endpoints will occur within k days. The first uses independent models for each of the endpoints. The second uses multi-label classification methods that take into account the interrelation between the labels. To my knowledge, this is the first approach that uses multi-label classification to perform prognosis prediction on ALS. The multi-label prognostic model obtained promising results to perform prognosis prediction, but further studies need to be performed to create a validation method for this kind of approach and to have an accurate comparison with the independent models.