Article

Understanding the chronic kidney disease landscape using patient representation learning from electronic health records

2022-10-26

Abstract excerpt

Understanding various subpopulations in chronic kidney disease can improve patient care and aid in developing treatments targeted to patients’ needs. Due to the general slow disease progression, electronic health records, which comprise a rich source of longitudinal real-world patient-level information, offer an approach for generating insights into disease. Here we apply the open-source ConvAE framework to train...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fc7b6939-206f-5c0f-a678-7caa858e5fc0
DOI: 10.1101/2022.10.25.22280440

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Understanding the chronic kidney disease landscape using patient representation learning from electronic health recordsDOI 10.1101/2022.10.25.22280440Read

DEPOT: graph learning delineates the roles of cancers in the progression trajectories of chronic kidney disease using electronic medical recordsSemantic proximity Read

Progression of chronic kidney disease in African Americans with type 2 diabetes mellitus using topology learning in electronic medical recordsSemantic proximity Read

Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studiesSemantic proximity Read

Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approachSemantic proximity Read

Deep Learning-Identified Clinical Trajectory Patterns and Associations with Kidney Outcomes in IgA NephropathySemantic proximity Read

Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks.Semantic proximity Read

Evaluation of a Chronic Kidney Disease e-Phenotype: Identification and Characterisation by Electronic Health Record Data.Semantic proximity Read

Artificial Intelligence for Chronic Kidney Disease Early Detection and PrognosisSemantic proximity Read

Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney DiseaseSemantic proximity Read

Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the<i>Rho</i>pathwaySemantic proximity Read

Exploratory electronic health record analysis with ehrapySemantic proximity Read

Integrated multi-omics approaches to improve classification of chronic kidney disease.Semantic proximity Read