Clinical Research Informatics: Contributions from 2016
- PMID: 29063566
- PMCID: PMC6239247
- DOI: 10.15265/IY-2017-024
Clinical Research Informatics: Contributions from 2016
Abstract
Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2016. Methods: A bibliographic search using a combination of MeSH and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 452 papers published in 2016 in the various areas of CRI and returned by the query, the full review process selected four best papers. The authors of the first paper utilized a comprehensive representation of the patient medical record and semi-automatically labeled training sets to create phenotype models via a machine learning process. The second selected paper describes an open source tool chain securely connecting ResearchKit compatible applications (Apps) to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). The third selected paper describes the FAIR Guiding Principles for scientific data management and stewardship. The fourth selected paper focuses on the evaluation of the risk of privacy breaches in releasing genomics datasets. Conclusions: A major trend in the 2016 publications is the variety of research on "real-world data" - healthcare-generated data, person health data, and patient-reported outcomes -highlighting the opportunities provided by new machine learning techniques as well as new potential risks of privacy breaches.
Georg Thieme Verlag KG Stuttgart.
Conflict of interest statement
Disclosure The authors report no conflicts of interest in this work.
References
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- OMOP Health Outcomes of Interest | Observational Medical Outcomes Partnership [Internet][cité 30 mai 2017]. Available at:http://omop.org/HOI.
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- Beaulieu-Jones BK, Greene CS. Pooled Resource Open-Access ALS Clinical Trials Consortium. Semi-supervised learning of the electronic health record for phenotype stratification. J Biomed Inform. 2016;64:168–78. - PubMed
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