Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study
|Title:||Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study||Authors:||Antoniadi, Anna Markella; Galvin, Miriam; Heverin, Mark; Hardiman, Orla; Mooney, Catherine||Permanent link:||http://hdl.handle.net/10197/11763||Date:||28-Feb-2020||Online since:||2020-12-01T15:24:22Z||Abstract:||OBJECTIVES:Amyotrophic 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. DESIGN:This 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 PARTICIPANTS:This 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. RESULTS:The 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. CONCLUSION:This 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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||BMJ||Journal:||BMJ Open||Volume:||10||Issue:||2||Copyright (published version):||2020 the Authors||Keywords:||Mental health; Clinical research; Neurosciences; Rare diseases; Neurodegenerative; ALS; Brain disorders; Depression; Behavioral and social science; Neurological||DOI:||10.1136/bmjopen-2019-033109||Language:||en||Status of Item:||Peer reviewed||ISSN:||2044-6055||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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