ArticleScientific reports2023
Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Explainable Artificial Intelligence for Rehospitalization and Financial Burden of Fertile Women in Orthopedic Care.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.Psychiatry investigation · 2024Article
- A novel explainable machine learning-based healthy ageing scale.BMC medical informatics and decision making · 2024Article
- Explainable artificial intelligence on safe balance and its major determinants in stroke patients.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Three pairs are considered, diabetes-cancer, diabetes-heart disease and diabetes-mental disease. Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 5527 participants aged 56 or more. The evaluation of the hypotheses were based on (1) whether diabetes and its comorbid condition in 2016 were top-5 determinants of the comorbidity in 2018 (hypothesis 1) and (2) whether top-10 determinants of the comorbidity in 2018 were similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Based on random forest variable importance, diabetes and its comorbid condition in 2016 were top-2 determinants of the comorbidity in 2018. Top-10 determinants of the comorbidity in 2018 were the same for different pairs of diabetes and its comorbid condition: body mass index, income, age, life satisfaction-health, life satisfaction-economic, life satisfaction-overall, subjective health and children alive in 2016. In terms of SHAP values, the probability of the comorbidity is expected to decrease by 0.02-0.03 in case life satisfaction overall is included to the model. This study supports the two hypotheses, highlighting the importance of preventive measures for body mass index, socioeconomic status, life satisfaction and family support to manage diabetes and its comorbid condition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.