Prediction of quality of life in people with ALS: on the road towards explainable clinical decision support
|Title:||Prediction of quality of life in people with ALS: on the road towards explainable clinical decision support||Authors:||Antoniadi, Anna Markella; Galvin, Miriam; Heverin, Mark; Hardiman, Orla; Mooney, Catherine||Permanent link:||http://hdl.handle.net/10197/13123||Date:||Jun-2021||Online since:||2022-09-20T15:29:59Z||Abstract:||Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).||Funding Details:||European Commission - European Regional Development Fund
Health Research Board
Science Foundation Ireland
|Funding Details:||FutureNeuro industry partners||Type of material:||Journal Article||Publisher:||ACM||Journal:||ACM SIGAPP Applied Computing Review||Volume:||21||Issue:||2||Start page:||5||End page:||17||Copyright (published version):||2021 the Authors||Keywords:||Amyotrophic lateral sclerosis; Quality of life; Clinical decision support system; Machine learning; Explainable artificial intelligence||DOI:||10.1145/3477127.3477128||Language:||en||Status of Item:||Peer reviewed||ISSN:||1559-6915||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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