Predicting Non-invasive Ventilation in ALS Patients Using Stratified Disease Progression Groups

Pires, Sofia; Gromicho, Marta; Pinto, Susana; Carvalho, Mamede de; Madeira, Sara C.

doi:10.1109/icdmw.2018.00113

Cited by 10 publications

(17 citation statements)

References 21 publications

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“…This data is collected from every participant at the baseline assessment, as well as on their quarterly follow-up consultations. For a detailed description of these variables we refer to [8].…”

Section: Dataset and Methods 21 Datamentioning

confidence: 99%

“…These correspond to the learning examples used to train the prognostic models. This learning approach using time windows was already used in [3,8], and allows to answer the question of "Will an ALS patient require NIV k days after the medical assessment?". However, these time windows are inclusive, in the sense that prognostic models built for a 365-days time window, for instance, might include cases requiring NIV either at 90 or 180 days after the assessment.…”

Section: 11mentioning

confidence: 99%

“…In this context, an early prediction of patients' need for assisted ventilation would have significant implications for patients' quality of life and health costs. Machine Learning-based approaches have been followed to tackle this problem [3,8]. Carreiro et al [3] developed a supervised learning approach to predict the need for NIV in ALS patients within specific time windows.…”

Section: Introductionmentioning

confidence: 99%

“…Carreiro et al [3] developed a supervised learning approach to predict the need for NIV in ALS patients within specific time windows. Furthermore, Pires et al [8] attempted to improve these predictive models by tackling the heterogeneity in ALS patients, using stratified disease progression groups.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, there are no previous studies on CP on ALS prediction, making it a relevant case study. Furthermore, we use a different way of building learning examples using time windows when compared with previous literature [3,8], by narrowing the time width of prediction.…”

Section: Introductionmentioning

confidence: 99%

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Predicting assisted ventilation in Amyotrophic Lateral Sclerosis using a mixture of experts and conformal predictors

Pereira,

Pires,

Gromicho

et al. 2019

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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clinical translation of such models is limited by the lack of insight 1) on the risk of error for predictions at patient-level, and 2) on the most adequate time to administer the non-invasive ventilation. To address these issues, we combine Conformal Prediction (a machine learning framework that complements predictions with confidence measures) and a mixture experts into a prognostic model which not only predicts whether an ALS patient will suffer from respiratory insufficiency but also the most likely time window of occurrence, at a given reliability level. Promising results were obtained, with near 80% of predictions being correctly identified.

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Section: Dataset and Methods 21 Datamentioning

confidence: 99%

Section: 11mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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