ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Using Unsupervised Clustering to Identify Pregnancy Co-Morbidities.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. 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 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.
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Who cites it
5 citing papers in PubMed.
- Group-Based Trajectory Modeling Identifies Distinct Patterns of Sympathetic Hyperactivity Following Traumatic Brain Injury.Neurocritical care · 2025Observational
- Group based trajectory modeling identifies distinct patterns of sympathetic hyperactivity following traumatic brain injury.Research square · 2024Article
- Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa.BMC infectious diseases · 2023Article
- An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- A Population-Based Study of Pre-Existing Health Conditions in Traumatic Brain Injury.Neurotrauma reports · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Absent a priori knowledge, unsupervised techniques identify meaningful clusters that can form the basis for subsequent analyses. This study explored the problem of inferring comorbidity-based profiles of complex diseases through unsupervised clustering methodologies. This study first considered the K-Modes algorithm, followed by, the self organizing map (SOM) technique to extract co-morbidity based clusters from a healthcare discharge dataset. After validation of general cluster composition for diabetes mellitus, co-morbidity based clusters were identified for pregnancy. The SOM technique was found to infer distinct clusterings of pregnancy ranging from normal birth to preterm birth, and potentially interesting comorbidities that could be validated by published literature The promising results suggest that the SOM technique is a valuable unsupervised clustering method for discovering co-morbidity based clusters.
Identifiers
31258983PMC6568081What OpenQuestion holds
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.