ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
An exploration of ontology-based EMR data abstraction for diabetic kidney disease prediction.
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 6 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
6 citing papers in PubMed, 8 citations in OpenAlex.
- Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data.PLOS digital health · 2025Article
- A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules.Health information science and systems · 2024Article
- Development of a knowledge mining approach to uncover heterogeneous risk predictors of acute kidney injury across age groups.International journal of medical informatics · 2022Article
- Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.Nature communications · 2020Article
- Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.BMC nephrology · 2020Article
- Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients Using a Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study.JMIR medical informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Diabetic Kidney Disease (DKD) is a critical and morbid complication of diabetes and the leading cause of chronic kidney disease in the developed world. Electronic medical records (EMRs) hold promise for supporting clinical decision-making with its nationwide adoption as well as rich information characterizing patients' health care experience. However, few retrospective studies have fully utilized the EMR data to model DKD risk. This study examines the effectiveness of an unbiased data driven approach in identifying potential DKD patients in 6 months prior to onset by utilizing EMR on a broader spectrum. Meanwhile, we evaluate how different levels of data granularity of Medications and Diagnoses observations would affect prediction performance and knowledge discovery. The experimental results suggest that different data granularity may not necessarily influence the prediction accuracy, but it would dramatically change the internal structure of the predictive models.
Indexed as
Identifiers
31259027PMC6568123W3004344903What 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.