ArticleBMC medical informatics and decision making2020
Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Global prevalence of autism spectrum disorder and its gastrointestinal symptoms: A systematic review and meta-analysis.Frontiers in psychiatry · 2022Pooled it
- How Might Comorbid Conditions Co-occurring With Child Autism Impact Parenting Stress?Journal of autism and developmental disorders · 2026Article
- Network Analysis of Legg-Calve-Perthes Disease and Its Comorbidities.Journal of clinical medicine · 2025Article
- Factors predicting parenting stress in the autism spectrum disorder context: A network analysis approach.PloS one · 2025Article
- Groups and Subgroups in Autism Spectrum Disorder (ASD) Considering an Advanced Integrative Model (AIM).Journal of personalized medicine · 2024Article
- Cumulative comorbidity between neurodevelopmental, internalising, and externalising disorders in childhood: a network approach.European child & adolescent psychiatry · 2024Article
- The regional disparities in liver disease comorbidity among elderly Chinese based on a health ecological model: the China Health and Retirement Longitudinal Study.BMC public health · 2024Article
- Phenotypic Disease Network Analysis to Identify Comorbidity Patterns in Hospitalized Patients with Ischemic Heart Disease Using Large-Scale Administrative Data.Healthcare (Basel, Switzerland) · 2022Article
- Rationale of an Advanced Integrative Approach Applied to Autism Spectrum Disorder: Review, Discussion and Proposal.Journal of personalized medicine · 2021Review
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAutism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism.
methodsWe used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children's Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search.
resultsWe found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported.
conclusionWe demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.
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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.