ArticleScientific data2014
Building the graph of medicine from millions of clinical narratives.
Article in Scientific data, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Inference of dependency knowledge graph for Electronic Health Records.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Integrating large scale genetic and clinical information to predict cases of heart failure.Communications medicine · 2025Article
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.JAMIA open · 2025Article
- Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.BMC bioinformatics · 2025Article
- Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label.Bioengineering (Basel, Switzerland) · 2024Article
- Patient-centric knowledge graphs: a survey of current methods, challenges, and applications.Frontiers in artificial intelligence · 2024Review
- Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Effects of vitamin D signaling in cardiovascular disease: centrality of macrophage polarization.Frontiers in cardiovascular medicine · 2024Article
- Machine learning and deep learning in medicine and neuroimaging.Annals of the Child Neurology Society · 2023Review
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- PPTPS: Building privacy-preserving auditable service with traceable timeliness for public cloud storage.PloS one · 2022Article
- Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data.NPJ digital medicine · 2021Article
- Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study.Journal of safety science and resilience = An quan ke xue yu ren xing (Ying wen) · 2021Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer.Frontiers in neurorobotics · 2021Article
- Automated ICD coding via unsupervised knowledge integration (UNITE).International journal of medical informatics · 2020Article
- Graph embedding on biomedical networks: methods, applications and evaluations.Bioinformatics (Oxford, England) · 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
3 authors.
Funding
Abstract
Electronic health records (EHR) represent a rich and relatively untapped resource for characterizing the true nature of clinical practice and for quantifying the degree of inter-relatedness of medical entities such as drugs, diseases, procedures and devices. We provide a unique set of co-occurrence matrices, quantifying the pairwise mentions of 3 million terms mapped onto 1 million clinical concepts, calculated from the raw text of 20 million clinical notes spanning 19 years of data. Co-frequencies were computed by means of a parallelized annotation, hashing, and counting pipeline that was applied over clinical notes from Stanford Hospitals and Clinics. The co-occurrence matrix quantifies the relatedness among medical concepts which can serve as the basis for many statistical tests, and can be used to directly compute Bayesian conditional probabilities, association rules, as well as a range of test statistics such as relative risks and odds ratios. This dataset can be leveraged to quantitatively assess comorbidity, drug-drug, and drug-disease patterns for a range of clinical, epidemiological, and financial applications.
Indexed as
Identifiers
What 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.