Antoniadi, Anna MarkellaAnna MarkellaAntoniadiGalvin, MiriamMiriamGalvinHeverin, MarkMarkHeverinHardiman, OrlaOrlaHardimanMooney, CatherineCatherineMooney2021-05-192021-05-192020 the A2020-09-219781450379649http://hdl.handle.net/10197/12180The aim of this study is to create a Clinical Decision Support System (CDSS) to assist in the early identification and support of caregivers at risk of experiencing burden while caring for a person with Amyotrophic Lateral Sclerosis. We work towards a system that uses a minimum amount of data that could be routinely collected. We investigated if the impairment of patients alone provides sufficient information for the prediction of caregiver burden. Results reveal a better performance of our system in identifying those at risk of high burden, but more information is needed for an accurate CDSS.en© 2020 the Authors. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, http://doi.acm.org/10.1145/3388440.3414908Amyotrophic lateral sclerosisMachine learningRandom forestCaregiver burdenConference Publication10.1145/3388440.34149082021-01-1616/RC/3948ICE/2012/6HRB-JPND/2013/117 CM-324https://creativecommons.org/licenses/by-nc-nd/3.0/ie/