ArticleNPJ digital medicine2021
Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence, explainable deep learning, and generative models for osteoporosis and bone disease prediction: a systematic review and future research roadmap.Frontiers in artificial intelligence · 2026Pooled it
- Medical code embeddings from claims-based co-occurrences: a unified semantic space for ICD-10 diagnoses and ATC medications.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Building a Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care: Protocol for a FAIR-EC Study.JMIR research protocols · 2026Article
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- A large-scale multi-ancestry mitochondrial variant association analysis for cardiometabolic traits.Nature communications · 2026Article
- LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation.Statistics in medicine · 2026Article
- Evaluating the feasibility of the CIPHER metadata framework towards building a conceptual phenotype standard.JAMIA open · 2026Article
- Estimate renal cell carcinoma recurrence rates using electronic health records.ESMO real world data and digital oncology · 2026Article
- Antidiabetic Drug Associations With Heart Failure Outcomes: Real-World Evidence Study Using Electronic Health Records.JMIR diabetes · 2026Article
- Phenotypic prediction of missense variants via deep contrastive learning.Nature biomedical engineering · 2026Article
- Inferring Rheumatoid Arthritis Disease Activity Status From the Electronic Health Records Across Health Systems.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Representation learning to advance multi-institutional studies with electronic health record data from US and France.Nature communications · 2026Article
- Inference of dependency knowledge graph for Electronic Health Records.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Finding the most promising indications for novel treatments in oncology.NPJ precision oncology · 2026Article
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- Integrating large scale genetic and clinical information to predict cases of heart failure.Communications medicine · 2025Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Automated Data Harmonization in Clinical Research: Natural Language Processing Approach.JMIR formative research · 2025Article
- A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.
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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.