ArticlePloS one2015
Ontology-based data integration between clinical and research systems.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 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
29 citing papers in PubMed.
- Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review.JMIR medical informatics · 2024Article
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening.PloS one · 2022Article
- Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.International journal of medical informatics · 2021Article
- A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study.JMIR medical informatics · 2021Article
- Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models.Journal of biomedical informatics · 2021Article
- Outlier concepts auditing methodology for a large family of biomedical ontologies.BMC medical informatics and decision making · 2020Article
- BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.Medical & biological engineering & computing · 2020Article
- A Semantic-Based Approach for Managing Healthcare Big Data: A Survey.Journal of healthcare engineering · 2020Review
- Feasibility analysis of conducting observational studies with the electronic health record.BMC medical informatics and decision making · 2019Article
- Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.Yearbook of medical informatics · 2019Article
- Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC.Applied clinical informatics · 2019Article
- Seeing the whole picture: integrated pre-surgery reports with PreOptique.Journal of biomedical semantics · 2019Article
- Merging heterogeneous clinical data to enable knowledge discovery.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools.Journal of healthcare engineering · 2019Article
- The role of medical registries, potential applications and limitations.Medicine and pharmacy reports · 2019Review
- Enabling semantic queries across federated bioinformatics databases.Database : the journal of biological databases and curation · 2019Article
- Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches.JMIR medical informatics · 2018Article
- The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources.Yearbook of medical informatics · 2018Review
- An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival.BMC medical informatics and decision making · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data from the electronic medical record comprise numerous structured but uncoded elements, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of relevant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.
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.