ArticleScientific reports2021
Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Identifying Risk Factors for Caregiver Burden in Neurological Disorders Using Machine Learning.Medical sciences (Basel, Switzerland) · 2026Observational
- Associations influencing quality of life in caregivers of patients with amyotrophic lateral sclerosis: a stress-process model approach.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Health-related quality of life and productivity burden for non-professional caregivers of adults with rare diseases: a real-world study.Orphanet journal of rare diseases · 2025Article
- Non-Pharmacological Interventions for Caregivers of People with Motor Neurone Disease: A Scoping Review of Psychosocial Outcomes.Brain sciences · 2025Review
- Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Experiences of quality of life and access to health services among rare disease caregivers: a scoping review.Orphanet journal of rare diseases · 2024Article
- Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Machine-learning model predicting quality of life using multifaceted lifestyles in middle-aged South Korean adults: a cross-sectional study.BMC public health · 2024Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- "Walking a tightrope": A grounded theory approach to informal caregiving for amyotrophic lateral sclerosis.Health & social care in the community · 2022Article
- Health-Related Quality of Life and Perceived Burden of Informal Caregivers of Patients with Rare Diseases in Selected European Countries.International journal of environmental research and public health · 2022Article
- A Clinical Decision Support System for the Prediction of Quality of Life in ALS.Journal of personalized medicine · 2022Article
- An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus.Scientific reports · 2022Article
- Psychological Support for Family Caregivers of Patients With Amyotrophic Lateral Sclerosis at the Time of the Coronavirus Disease 2019 Pandemic: A Pilot Study Using a Telemedicine Approach.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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