Literature DB >> 22797033

Using Australian medicines terminology (AMT) and SNOMED CT-AU to better support clinical research.

Simon J McBride1, Michael J Lawley, Hugo Leroux, Simon Gibson.   

Abstract

A large scale, long term clinical study faced significant quality issues with its medications use data which had been collected from participants using paper forms and manually entered into a data capture system. A method was developed that automatically mapped 72.2% of the unique medication names collected for the study to the AMT and SNOMED CT-AU using Ontoserver, a terminology server for clinical ontologies. These initial results are promising and, with further improvements to the algorithms and evaluation, are expected to greatly improve the analysis of medication data gathered from the study.

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Year:  2012        PMID: 22797033

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  4 in total

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Authors:  Li Wang; Yaoyun Zhang; Min Jiang; Jingqi Wang; Jiancheng Dong; Yun Liu; Cui Tao; Guoqian Jiang; Yi Zhou; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2018-07-01       Impact factor: 4.497

2.  Semantic enrichment of longitudinal clinical study data using the CDISC standards and the semantic statistics vocabularies.

Authors:  Hugo Leroux; Laurent Lefort
Journal:  J Biomed Semantics       Date:  2015-04-09

3.  Modelling Medications for Public Health Research.

Authors:  D van Gaans; S Ahmed; K D'Onise; J Moyon; G Caughey; R McDermott
Journal:  Online J Public Health Inform       Date:  2016-09-15

4.  An Internet-Based Method for Extracting Nursing Home Resident Sedative Medication Data From Pharmacy Packing Systems: Descriptive Evaluation.

Authors:  Tristan Ling; Peter Gee; Juanita Westbury; Ivan Bindoff; Gregory Peterson
Journal:  J Med Internet Res       Date:  2017-08-03       Impact factor: 5.428

  4 in total

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