ArticleBMJ open2020
Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multidisciplinary care in Amyotrophic Lateral Sclerosis: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Pooled it
- Identifying Risk Factors for Caregiver Burden in Neurological Disorders Using Machine Learning.Medical sciences (Basel, Switzerland) · 2026Observational
- Social determinants of health to predict health-related quality of life in diabetes patients: explainable machine learning approaches.Endocrine · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors.Healthcare (Basel, Switzerland) · 2024Article
- Validation of depression determinants in caregivers of dementia patients with machine learning algorithms and statistical model.Frontiers in medicine · 2023Article
- Understanding Cancer Caregiving and Predicting Burden: An Analytics and Machine Learning Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A Clinical Decision Support System for the Prediction of Quality of Life in ALS.Journal of personalized medicine · 2022Article
- Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.Journal of medical Internet research · 2021Review
- Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia.Heliyon · 2021Article
- Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.Scientific reports · 2021Article
- Informal Caregiving in Amyotrophic Lateral Sclerosis (ALS): A High Caregiver Burden and Drastic Consequences on Caregivers' Lives.Brain sciences · 2021Article
- Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore.Applied clinical informatics · 2021Article
- The effects of family follow-up nursing on elderly cognitive impairment patients' Barthel index scores and mental statuses.American journal of translational research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAmyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that is characterised by the rapid degeneration of upper and lower motor neurons and has a fatal trajectory 3-4 years from symptom onset. Due to the nature of the condition patients with ALS require the assistance of informal caregivers whose task is demanding and can lead to high feelings of burden. This study aims to predict caregiver burden and identify related features using machine learning techniques.
designThis included demographic and socioeconomic information, quality of life, anxiety and depression questionnaires, for patients and carers, resource use of patients and clinical information. The method used for prediction was the Random forest algorithm. SETTING AND
participantsThis study investigates a cohort of 90 patients and their primary caregiver at three different time-points. The patients were attending the National ALS/Motor Neuron Disease Multidisciplinary Clinic at Beaumont Hospital, Dublin.
resultsThe caregiver's quality of life and psychological distress were the most predictive features of burden (0.92 sensitivity and 0.78 specificity). The most predictive features for Clinical Decision Support model were associated with the weekly caregiving duties of the primary caregiver as well as their age and health and also the patient's physical functioning and age of onset. However, this model had a lower sensitivity and specificity score (0.84 and 0.72, respectively). The ability of patients without gastrostomy to cut food and handle utensils was also highly predictive of burden in this study. Generally, our models are better in predicting the high-risk category, and we suggest that information related to the caregiver's quality of life and psychological distress is required.
conclusionThis work demonstrates a proof of concept of an informatics solution to identifying caregivers at risk of burden that could be incorporated into future care pathways.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.