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. Sep-Oct 2013;20(5):954-61.
doi: 10.1136/amiajnl-2012-001431. Epub 2013 Apr 10.

Development and Evaluation of an Ensemble Resource Linking Medications to Their Indications

Free PMC article

Development and Evaluation of an Ensemble Resource Linking Medications to Their Indications

Wei-Qi Wei et al. J Am Med Inform Assoc. .
Free PMC article


Objective: To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs).

Materials and methods: We processed four public medication resources, RxNorm, Side Effect Resource (SIDER) 2, MedlinePlus, and Wikipedia, to create MEDI. We applied natural language processing and ontology relationships to extract indications for prescribable, single-ingredient medication concepts and all ingredient concepts as defined by RxNorm. Indications were coded as Unified Medical Language System (UMLS) concepts and International Classification of Diseases, 9th edition (ICD9) codes. A total of 689 extracted indications were randomly selected for manual review for accuracy using dual-physician review. We identified a subset of medication-indication pairs that optimizes recall while maintaining high precision.

Results: MEDI contains 3112 medications and 63 343 medication-indication pairs. Wikipedia was the largest resource, with 2608 medications and 34 911 pairs. For each resource, estimated precision and recall, respectively, were 94% and 20% for RxNorm, 75% and 33% for MedlinePlus, 67% and 31% for SIDER 2, and 56% and 51% for Wikipedia. The MEDI high-precision subset (MEDI-HPS) includes indications found within either RxNorm or at least two of the three other resources. MEDI-HPS contains 13 304 unique indication pairs regarding 2136 medications. The mean±SD number of indications for each medication in MEDI-HPS is 6.22 ± 6.09. The estimated precision of MEDI-HPS is 92%.

Conclusions: MEDI is a publicly available, computable resource that links medications with their indications as represented by concepts and billing codes. MEDI may benefit clinical EMR applications and reuse of EMR data for research.

Keywords: International Classification of Diseases; Ontology; Terminology; Unified Medical Language System; electronic medical records; medication indications.


Figure 1
Figure 1
Flowchart for MEDication–Indication (MEDI) creation. HPS, high-precision subset; ICD9, International Classification of Diseases, 9th edition; KMCI, KnowledgeMap Concept Indexer; RxCUI, RxNorm concept unique identifier; SIDER, Side Effect Resource.
Figure 2
Figure 2
Weighted Venn diagram of the distribution of 3112 medications (left) and 63 343 indication pairs (right) within the four resources. Each border color represents a resource. Different colored areas represent medications–indications that were found within different combinations of resources. The area sizes surrounded by border color(s) are proportional to the number of medications–indications that were found within the corresponding resource(s). SIDER, Side Effect Resource.

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