ArticleBioMed research international2017
Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Graph representation and learning for drug administration prediction.BMC medical informatics and decision making · 2026Article
- Research information systems and knowledge graphs: a review.Frontiers in research metrics and analytics · 2026Review
- Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model.Frontiers in plant science · 2024Article
- The importance of graph databases and graph learning for clinical applications.Database : the journal of biological databases and curation · 2023Review
- Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study.Journal of medical Internet research · 2023Article
- Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution.BMC bioinformatics · 2022Article
- A semantic web technology index.Scientific reports · 2022Article
- MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records.Frontiers in genetics · 2022Article
- Predicting the relationships between gut microbiota and mental disorders with knowledge graphs.Health information science and systems · 2021Article
- Leveraging graph-based hierarchical medical entity embedding for healthcare applications.Scientific reports · 2021Article
- Network graph representation of COVID-19 scientific publications to aid knowledge discovery.BMJ health & care informatics · 2021Article
- KGen: a knowledge graph generator from biomedical scientific literature.BMC medical informatics and decision making · 2020Article
- Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation.JMIR medical informatics · 2020Article
- KGHC: a knowledge graph for hepatocellular carcinoma.BMC medical informatics and decision making · 2020Article
- A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development.JMIR medical informatics · 2020Article
- Artificial Intelligence Pipeline to Bridge the Gap between Bench Researchers and Clinical Researchers in Precision Medicine.Med one · 2020Article
- Knowledge Extraction of Cohort Characteristics in Research Publications.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Personalized Health Knowledge Graph.CEUR workshop proceedings · 2018Article
- As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018.Yearbook of medical informatics · 2018Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.
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.