ArticleScience progress
Interpretable machine learning in type-2 diabetes prediction in patients with depressive symptoms: Insights from mendelian randomization and physical activity.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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0 citing papers in PubMed.
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Authors and funding
4 authors.
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Abstract
ObjectiveThis study aimed to examine the causal relationship between depression and type-2 diabetes (T2D) and to develop a machine-learning (ML)-driven risk prediction model for T2D.MethodsThis cross-sectional study performed forward and reverse Mendelian randomization (MR) and multivariable MR (MVMR) analyses using GWAS summary data. Data from the National Health and Nutrition Examination Survey 2007-2018 were used to develop a ML-based T2D risk prediction model among individuals with depressive symptoms (
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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.