ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Interpretable Sub-phenotype Identification in Acute Kidney Injury.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
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Who cites it
3 citing papers in PubMed.
- Electronic Phenotype for Detection, Staging, and Subtyping of Acute Kidney Injury.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- A Machine Learning Prediction Model for In-Hospital Mortality in Postoperative Patients With Type A Aortic Dissection.Reviews in cardiovascular medicine · 2026Article
- Temporal Rule Mining for Enhanced Risk Pattern Extraction: A Case Study with Acute Kidney Injury.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Acute kidney injury (AKI) is a life-threatening and heterogeneous syndrome. Timely and etiology-based personalized treatment is crucial. AKI sub-phenotyping can lead to better understanding of the pathophysiology of AKI and help developing more targeted intervention. Current dimensionality reduction and similarity-based clustering for AKI sub-phenotyping suffer from limited interpretability and specificity. To address these limitations, we propose a pattern mining approach with multiobjective evolutionary algorithm (MOEA) for AKI sub-phenotyping. AKI sub-phenotypes are presented as explicit rules, so no post-hoc explanation is needed. Also, our method can search feature subspace efficiently for minor and highly specific sub-phenotypes. We aimed to discover sub-phenotypes for AKI patients against non-AKI patients (AKI vs non-AKI) and moderate-to-severe AKI patients against mild AKI patients (AKI-2/3 vs AKI-1). We identified 174(178) significant sub-phenotypes with average confidence of 0.33(0.33). Our method can assign patients to a sub-phenotype with higher confidence than k-means clustering, with average improvement of 0.20(0.23).
Indexed as
Identifiers
37128400PMC10148290What Socratic 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 Socratic graph.