ReviewJournal of biomedical informatics2021
Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.
Review in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
26 citing papers in PubMed, 42 citations in OpenAlex.
- Article
- Recovery Patterns: Longitudinal Cluster Analysis of Physical Function Following Abdominal Surgery.Annals of surgery · 2026Article
- Long COVID longitudinal symptoms burden clusters within a national community-based cohort.BMC infectious diseases · 2026Article
- High-Frequency Functional Trajectories Predict Depressive Worsening in Singapore's Community-Dwelling Older Adults.Healthcare (Basel, Switzerland) · 2026Article
- Defining and validating a multidimensional digital metric of health states in chronic back and leg pain.NPJ digital medicine · 2025Article
- A Challenge To The Assumption That Short- versus Long-Access Groups of Opioid Users Represent Distinct Phenotypes.bioRxiv : the preprint server for biology · 2025Article
- Long COVID Longitudinal Symptoms Burden Clusters Within A National Community-Based Cohort.medRxiv : the preprint server for health sciences · 2025Article
- Modeling opioid overdose events recurrence with a covariate-adjusted triggering point process.PLoS computational biology · 2025Article
- Change trends in serum phosphate levels predict in-hospital mortality in critically ill septic patients.Scientific reports · 2025Article
- Surgical frequency analysis of patients clustered according to postoperative pain trajectory: a retrospective study.Scientific reports · 2025Article
- Longitudinal Viral Load Clustering for People With HIV Using Functional Principal Component Analysis.AIDS research and treatment · 2025Article
- Environmentally derived subgroups of preadolescents with family history of substance use exhibit distinct patterns of psychopathology and reward-related behaviors: insights from the ABCD study.Frontiers in child and adolescent psychiatry · 2025Article
- Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.Current medicinal chemistry · 2025Review
- Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records.Nature communications · 2024Article
- Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study.JMIR medical informatics · 2024Article
- Soft phenotyping for sepsis via EHR time-aware soft clustering.Journal of biomedical informatics · 2024Article
- Personalized event prediction for Electronic Health Records.Artificial intelligence in medicine · 2023Article
- Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis.Scientific reports · 2023Article
- Pain severity scale: A methodology for classifying postoperative pain severity by surgical procedure.Surgery open science · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
Abstract
Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.
Indexed as
Identifiers
What 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.