Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning

Abstract Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify caregivers whose quality of life (QoL) may...

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Autores principales:	Anna Markella Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, Catherine Mooney
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6da09fee5fc346bdbfd7159d281bf5b7
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spelling	oai:doaj.org-article:6da09fee5fc346bdbfd7159d281bf5b72021-12-02T15:03:07ZPrediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning10.1038/s41598-021-91632-22045-2322https://doaj.org/article/6da09fee5fc346bdbfd7159d281bf5b72021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91632-2https://doaj.org/toc/2045-2322Abstract Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify caregivers whose quality of life (QoL) may be impacted as a result of caring for a person with ALS. In this study, we worked towards the identification of the predictors of a caregiver’s QoL in addition to the development of a model for clinical use to alert clinicians when a caregiver is at risk of experiencing low QoL. The data were collected through the Irish ALS Registry and via interviews on several topics with 90 patient and caregiver pairs at three time-points. The McGill QoL questionnaire was used to assess caregiver QoL—the MQoL Single Item Score measures the overall QoL and was selected as the outcome of interest in this work. The caregiver’s existential QoL and burden, as well as the patient’s depression and employment before the onset of symptoms were the features that had the highest impact in predicting caregiver quality of life. A small subset of features that could be easy to collect was used to develop a second model to use it in a clinical setting. The most predictive features for that model were the weekly caregiving duties, age and health of the caregiver, as well as the patient’s physical functioning and age of onset.Anna Markella AntoniadiMiriam GalvinMark HeverinOrla HardimanCatherine MooneyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anna Markella Antoniadi Miriam Galvin Mark Heverin Orla Hardiman Catherine Mooney Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
description	Abstract Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify caregivers whose quality of life (QoL) may be impacted as a result of caring for a person with ALS. In this study, we worked towards the identification of the predictors of a caregiver’s QoL in addition to the development of a model for clinical use to alert clinicians when a caregiver is at risk of experiencing low QoL. The data were collected through the Irish ALS Registry and via interviews on several topics with 90 patient and caregiver pairs at three time-points. The McGill QoL questionnaire was used to assess caregiver QoL—the MQoL Single Item Score measures the overall QoL and was selected as the outcome of interest in this work. The caregiver’s existential QoL and burden, as well as the patient’s depression and employment before the onset of symptoms were the features that had the highest impact in predicting caregiver quality of life. A small subset of features that could be easy to collect was used to develop a second model to use it in a clinical setting. The most predictive features for that model were the weekly caregiving duties, age and health of the caregiver, as well as the patient’s physical functioning and age of onset.
format	article
author	Anna Markella Antoniadi Miriam Galvin Mark Heverin Orla Hardiman Catherine Mooney
author_facet	Anna Markella Antoniadi Miriam Galvin Mark Heverin Orla Hardiman Catherine Mooney
author_sort	Anna Markella Antoniadi
title	Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
title_short	Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
title_full	Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
title_fullStr	Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
title_full_unstemmed	Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
title_sort	prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6da09fee5fc346bdbfd7159d281bf5b7
work_keys_str_mv	AT annamarkellaantoniadi predictionofcaregiverqualityoflifeinamyotrophiclateralsclerosisusingexplainablemachinelearning AT miriamgalvin predictionofcaregiverqualityoflifeinamyotrophiclateralsclerosisusingexplainablemachinelearning AT markheverin predictionofcaregiverqualityoflifeinamyotrophiclateralsclerosisusingexplainablemachinelearning AT orlahardiman predictionofcaregiverqualityoflifeinamyotrophiclateralsclerosisusingexplainablemachinelearning AT catherinemooney predictionofcaregiverqualityoflifeinamyotrophiclateralsclerosisusingexplainablemachinelearning
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Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning

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