Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

Mohanad M Alsaleh; Freya Allery; Jung Won Choi; Tuankasfee Hama; Andrew McQuillin; Honghan Wu; Johan H Thygesen

doi:10.1016/j.ijmedinf.2023.105088

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

Int J Med Inform. 2023 Jul:175:105088. doi: 10.1016/j.ijmedinf.2023.105088. Epub 2023 May 4.

Authors

Mohanad M Alsaleh¹, Freya Allery², Jung Won Choi², Tuankasfee Hama², Andrew McQuillin³, Honghan Wu², Johan H Thygesen²

Affiliations

¹ Institute of Health Informatics, University College London, London, UK; Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah, Saudi Arabia. Electronic address: mohanad.alsaleh.21@ucl.ac.uk.
² Institute of Health Informatics, University College London, London, UK.
³ Division of Psychiatry, University College London, London, UK.

PMID: 37156169
DOI: 10.1016/j.ijmedinf.2023.105088

Abstract

Objective: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models.

Materials and methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling.

Results: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias.

Discussion: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons.

Conclusion: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities.

Keywords: Comorbidity; Explainable artificial intelligence; Machine learning; Multimorbidity; Prediction; Predictive modelling; Systematic review.

Publication types

Systematic Review
Review

MeSH terms

Artificial Intelligence*
Comorbidity
Eligibility Determination
Humans
Machine Learning
Quality of Life*