Development of an explainable clinical decision support system for the prediction of patient quality of life in amyotrophic lateral sclerosis
Open accessAbstract
Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative and currently incurable disease. It causes a rapid decline in motor functions and has a fatal trajectory. The aim of the treatment is mostly to alleviate symptoms and improve the patient's quality of life (QoL). The goal of this study is to develop a Clinical Decision Support System (CDSS) in order to alert clinicians when a patient is at risk of experiencing a low QoL, so that they are better supported. The source of the 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. The method we used for the development of the predictive models was Extreme Gradient Boosting (XGBoost), which was compared to a logistic regression baseline model. We used the SHAP (SHapley Additive exPlanations) values 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 by three features, with a F1-score on the test set equal to 0.81, a recall score of 0.78, and a precision score of 0.84, while, the addition of two features produced similar outcomes (0.79, 0.70 and 0.90 respectively). The three most important features were the age at disease onset, ALSFRS score for orthopnoea and the caregiver's status pre-caregiving.
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We might surpass the problem of data availability and heterogeneity by using the least amount of data available and the easiest-to-collect information to initiate the development of the system.
“Additionally, we might surpass the problem of data availability and heterogeneity by using the least amount of data available and the easiest-to-collect information to initiate the development of the system [140].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [140]
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- Authors
- Antoniadi, A. M., Galvin, M., Heverin, M., Hardiman, O., & Mooney, C.
- Affiliations
- University College Dublin (IE)
- Trinity College Dublin (IE)
- Venue
- Proceedings of the 36th Annual ACM Symposium on Applied Computing
- Publisher
- ACM
- Published
- 2021-03-22
- Pages
- 594-602
- OpenAlex
- W3157158784
- Cited by
- 12
- References
- 30
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 2 · 2021: 2
- FWCI
- 12.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Symposium on Applied Computing (publishedVersion, cc-by)
- Research Repository UCD (University College Dublin) (submittedVersion, cc-by)
- Funders
- Science Foundation Ireland (16/RC/3948)
- Irish Health Research Board (HRB) (ICE/2012/6 and HRB-JPND/2013/1)
- American ALS Association (17 CM-324)
- Topics
- Health Sciences › Medicine › Neurology › Amyotrophic Lateral Sclerosis Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- amyotrophic lateral sclerosis, quality of life prediction, clinical decision support systems, explainable AI, SHAP, XGBoost, ALSFRS, caregiver status, age of onset, orthopnea
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