A Clinical Decision Support System for the Prediction of Quality of Life in ALS.

ALS: Amyotrophic Lateral Sclerosis CDSS Explainable AI MND XAI artificial intelligence clinical decision support systems explainability machine learning quality of life

Journal

Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269

Informations de publication

Date de publication:
10 Mar 2022
Historique:
received: 23 11 2021
revised: 04 03 2022
accepted: 07 03 2022
entrez: 25 3 2022
pubmed: 26 3 2022
medline: 26 3 2022
Statut: epublish

Résumé

Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype Clinical Decision Support System (CDSS) to alert clinicians when a person with ALS is experiencing low QoL in order to inform and personalise the support they receive. Explainability is important for the success of a CDSS and its acceptance by healthcare professionals. The aim of this work isto announce our prototype (C-ALS), supported by a first short evaluation of its explainability. Given the lack of similar studies and systems, this work is a valid proof-of-concept that will lead to future work. We developed a CDSS that was evaluated by members of the team of healthcare professionals that provide care to people with ALS in the ALS/MND Multidisciplinary Clinic in Dublin, Ireland. We conducted a user study where participants were asked to review the CDSS and complete a short survey with a focus on explainability. Healthcare professionals demonstrated some uncertainty in understanding the system's output. Based on their feedback, we altered the explanation provided in the updated version of our CDSS. C-ALS provides local explanations of its predictions in a

Identifiants

pubmed: 35330435
pii: jpm12030435
doi: 10.3390/jpm12030435
pmc: PMC8955774
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Science Foundation Ireland
ID : 16/RC/3948
Pays : Ireland
Organisme : Irish Health Research Board
ID : ICE/2012/6 JPND/2013/1
Organisme : American ALS Association
ID : 17 CM-324

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Auteurs

Anna Markella Antoniadi (AM)

UCD School of Computer Science, University College Dublin, Dublin 4, Ireland.
FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.

Miriam Galvin (M)

Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland.

Mark Heverin (M)

Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland.

Lan Wei (L)

UCD School of Computer Science, University College Dublin, Dublin 4, Ireland.

Orla Hardiman (O)

FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.
Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland.
Department of Neurology, National Neuroscience Centre, Beaumont Hospital, Dublin 9, Ireland.

Catherine Mooney (C)

UCD School of Computer Science, University College Dublin, Dublin 4, Ireland.
FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.

Classifications MeSH