ArticleNature communications2019
Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed.
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- Graph representation and learning for drug administration prediction.BMC medical informatics and decision making · 2026Article
- MetagenomicKG: a knowledge graph for metagenomic applications.Bioinformatics (Oxford, England) · 2026Article
- A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases.medRxiv : the preprint server for health sciences · 2026Article
- A Focused Comparative Review of Innovative Therapeutics Across Autoimmune and Chronic Inflammatory Diseases.Life (Basel, Switzerland) · 2026Review
- DNA framework-based molecular transformer for logic-driven precision diagnostics.Science advances · 2026Article
- Investigating CAR-T Treatment Access for Multiple Myeloma Patients Using Real-World Evidence.Cancers · 2026Article
- Large language models improve transferability of electronic health record-based predictions across countries and coding systems.NPJ digital medicine · 2026Article
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.Nature communications · 2025Article
- Knowledge graph-aided Bayesian active learning for top-K genetic interaction discovery.Scientific reports · 2025Article
- Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies.BMC medical research methodology · 2025Article
- The Barancik award lecture: Multi-disciplinary research will be the key to stop, restore, and end MS.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis.Scientific reports · 2025Article
- Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.bioRxiv : the preprint server for biology · 2024Article
- Biomedical knowledge graph-optimized prompt generation for large language models.Bioinformatics (Oxford, England) · 2024Article
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- Medical-informed machine learning: integrating prior knowledge into medical decision systems.BMC medical informatics and decision making · 2024Review
- An open source knowledge graph ecosystem for the life sciences.Scientific data · 2024Article
- The transition from genomics to phenomics in personalized population health.Nature reviews. Genetics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.
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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.