ReviewInterdisciplinary sciences, computational life sciences2025
Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN.
Review in Interdisciplinary sciences, computational life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- A disease potential-driven graph attention model for comorbidity risk prediction of hypertension.Frontiers in big data · 2026Article
- ProstaNet: A Novel Geometric Vector Perceptrons-Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation.Research (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
purposeWith the evolving disease spectrum, chronic diseases have emerged as a primary burden and a leading cause of mortality. Due to the aging population and the nature of chronic illnesses, patients often suffer from multimorbidity. Predicting the likelihood of these patients developing specific diseases in the future based on their current health status and age factors is a crucial task in multimorbidity research.
methodsWe propose an algorithm, CE-GCN, which integrates age sequence and embeds Graph Convolutional Network (GCN) into Gated Recurrent Unit (GRU), utilizing the topological feature of network common neighbors to predict links in dynamic complex networks. First, we constructed a disease evolution network spanning from ages 45 to 90 years old using disease information from 3333 patients. Then, we introduced an innovative approach for link prediction aimed at uncovering relationships between various diseases. This method takes into account patients' age to construct the evolutionary structure of the disease network, thereby predicting the connections between chronic diseases.
resultsResults from experiments conducted on real networks indicate that our model surpasses others regarding both MRR and MAP. The proposed method accurately reveals associations between diseases and effectively captures future disease risks.
conclusionOur model can serve as an objective and convenient computer-aided tool to identify hidden relationships between diseases in order to assist healthcare professionals in taking early disease interventions, which can substantially lower the costs associated with treating multimorbidity and enhance the quality of life for patients suffering from chronic conditions.
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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 Socratic graph.