Journal article
Feasibility of Extracting Key Elements from ClinicalTrials.gov to Support Clinicians' Patient Care Decisions
AMIA ... Annual Symposium proceedings, v 2016, pp 705-714
2016
PMID: 28269867
Abstract
Clinicians need up-to-date evidence from high quality clinical trials to support clinical decisions. However, applying evidence from the primary literature requires significant effort.
To examine the feasibility of automatically extracting key clinical trial information from ClinicalTrials.gov.
We assessed the coverage of ClinicalTrials.gov for high quality clinical studies that are indexed in PubMed. Using 140 random ClinicalTrials.gov records, we developed and tested rules for the automatic extraction of key information.
The rate of high quality clinical trial registration in ClinicalTrials.gov increased from 0.2% in 2005 to 17% in 2015. Trials reporting results increased from 3% in 2005 to 19% in 2015. The accuracy of the automatic extraction algorithm for 10 trial attributes was 90% on average. Future research is needed to improve the algorithm accuracy and to design information displays to optimally present trial information to clinicians.
Metrics
10 Record Views
Details
- Title
- Feasibility of Extracting Key Elements from ClinicalTrials.gov to Support Clinicians' Patient Care Decisions
- Creators
- Heejun Kim - School of Information and Library Science, University of North Carolina, Chapel Hill,NC, USAJiantao Bian - Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USAJaved Mostafa - School of Information and Library Science, University of North Carolina, Chapel Hill,NC, USASiddhartha Jonnalagadda - Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USAGuilherme Del Fiol - Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Publication Details
- AMIA ... Annual Symposium proceedings, v 2016, pp 705-714
- Conference
- AMIA Annual Symposium
- Number of pages
- 1
- Grant note
- T15 LM007124 / NLM NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Scopus ID
- 2-s2.0-85026709011
- Other Identifier
- 991019189173904721