Literature DB >> 24303333

Development of an ensemble resource linking MEDications to their Indications (MEDI).

Wei-Qi Wei1, Robert M Cronin, Hua Xu, Thomas A Lasko, Lisa Bastarache, Joshua C Denny.   

Abstract

Understanding of medications-disease relationships is critical to distinguish indications from adverse effects, and medication exposures serve as important markers of disease and severity in electronic medical records (EMR). We created a computable medication-indication (MEDI) resource by applying natural language processing and ontology relationships to four public medication resources. Physicians evaluated accuracy of medication-indication relationships. MEDI contained 3,112 medications and 63,343 medication-indication pairs derived from the four resources, whose precisions varied from 56-94%. The MEDI high precision subset (MEDI-HPS) includes indications found within either RxNorm or ≥2 resources and had an estimated precision of 92%. MEDI-HPS contains 13,304 unique indication pairs for 2,136 medications. MEDI is a free, computable resource that links medications with their indications as represented by formal concepts and may assist clinical and research uses of EMR data.

Year:  2013        PMID: 24303333

Source DB:  PubMed          Journal:  AMIA Jt Summits Transl Sci Proc


  4 in total

1.  Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.

Authors:  Wei-Qi Wei; Pedro L Teixeira; Huan Mo; Robert M Cronin; Jeremy L Warner; Joshua C Denny
Journal:  J Am Med Inform Assoc       Date:  2015-09-02       Impact factor: 4.497

2.  Extracting research-quality phenotypes from electronic health records to support precision medicine.

Authors:  Wei-Qi Wei; Joshua C Denny
Journal:  Genome Med       Date:  2015-04-30       Impact factor: 11.117

3.  Automated detection of off-label drug use.

Authors:  Kenneth Jung; Paea LePendu; William S Chen; Srinivasan V Iyer; Ben Readhead; Joel T Dudley; Nigam H Shah
Journal:  PLoS One       Date:  2014-02-19       Impact factor: 3.240

4.  Desiderata for computable representations of electronic health records-driven phenotype algorithms.

Authors:  Huan Mo; William K Thompson; Luke V Rasmussen; Jennifer A Pacheco; Guoqian Jiang; Richard Kiefer; Qian Zhu; Jie Xu; Enid Montague; David S Carrell; Todd Lingren; Frank D Mentch; Yizhao Ni; Firas H Wehbe; Peggy L Peissig; Gerard Tromp; Eric B Larson; Christopher G Chute; Jyotishman Pathak; Joshua C Denny; Peter Speltz; Abel N Kho; Gail P Jarvik; Cosmin A Bejan; Marc S Williams; Kenneth Borthwick; Terrie E Kitchner; Dan M Roden; Paul A Harris
Journal:  J Am Med Inform Assoc       Date:  2015-09-05       Impact factor: 4.497

  4 in total

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