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Use of RxNorm and NDF-RT to normalize and characterize participant-reported medications in an i2b2-based research repository.

Blach C, Del Fiol G, Dundee C, Frund J, Richesson R, Smerek M, Walden A, Tenenbaum JD - AMIA Jt Summits Transl Sci Proc (2014)

Bottom Line: In order to maximize utility of drug data, while minimizing cost due to manual expert intervention, we have developed a generalizable approach to automatically coding medication data using RxNorm and NDF-RT and their associated application program interfaces (APIs).This approach has enabled use of drug data in combination with other complementary information for cohort identification within an i2b2-based participant registry.The method may be generalized to other projects requiring coding of medication data from free-text.

View Article: PubMed Central - PubMed

Affiliation: Duke University, Durham, NC.

ABSTRACT
The MURDOCK Study is longitudinal, large-scale epidemiological study for which participants' medication use is collected as free text. In order to maximize utility of drug data, while minimizing cost due to manual expert intervention, we have developed a generalizable approach to automatically coding medication data using RxNorm and NDF-RT and their associated application program interfaces (APIs). Of 130,273 entries, we were able to accurately map 122,523 (94%) to RxNorm concepts, and 106,135 (85%) of those drug concepts to nodes under the Drug by VA Class branch of NDF-RT. This approach has enabled use of drug data in combination with other complementary information for cohort identification within an i2b2-based participant registry. The method may be generalized to other projects requiring coding of medication data from free-text.

No MeSH data available.


Distribution of input terms across scoring categories. A. Perfect matches; B: Score == 100 for exactly 1 term, and that one is non-proprietary; C: Score == 100 for more than 1, and winner is non-proprietary; D: Score == 100 for proprietary only (whether 1 or more); E1: 75 ≤ Match score < 100; E2: 50 ≤ Match score < 75; E3 Match score < 50; F: No match found.
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f2-1861340: Distribution of input terms across scoring categories. A. Perfect matches; B: Score == 100 for exactly 1 term, and that one is non-proprietary; C: Score == 100 for more than 1, and winner is non-proprietary; D: Score == 100 for proprietary only (whether 1 or more); E1: 75 ≤ Match score < 100; E2: 50 ≤ Match score < 75; E3 Match score < 50; F: No match found.

Mentions: Out of 9432 participants, 8356 indicated taking one or more medications (including OTC medications, vitamins, and supplements) at one or more time points. The terms entered largely did not include dosages or delivery mechanism. This resulted in 130,273 total (18,924 unique) drug name entries. As illustrated in Figure 2, 99,538 entries (76%) of terms were perfect matches. On the other hand, the majority of the unique terms fell into category E (14,114 out of 18,924; 75%). This was to be expected as there are a number of different incorrect ways to spell a given drug name, and only one correct way.


Use of RxNorm and NDF-RT to normalize and characterize participant-reported medications in an i2b2-based research repository.

Blach C, Del Fiol G, Dundee C, Frund J, Richesson R, Smerek M, Walden A, Tenenbaum JD - AMIA Jt Summits Transl Sci Proc (2014)

Distribution of input terms across scoring categories. A. Perfect matches; B: Score == 100 for exactly 1 term, and that one is non-proprietary; C: Score == 100 for more than 1, and winner is non-proprietary; D: Score == 100 for proprietary only (whether 1 or more); E1: 75 ≤ Match score < 100; E2: 50 ≤ Match score < 75; E3 Match score < 50; F: No match found.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4333688&req=5

f2-1861340: Distribution of input terms across scoring categories. A. Perfect matches; B: Score == 100 for exactly 1 term, and that one is non-proprietary; C: Score == 100 for more than 1, and winner is non-proprietary; D: Score == 100 for proprietary only (whether 1 or more); E1: 75 ≤ Match score < 100; E2: 50 ≤ Match score < 75; E3 Match score < 50; F: No match found.
Mentions: Out of 9432 participants, 8356 indicated taking one or more medications (including OTC medications, vitamins, and supplements) at one or more time points. The terms entered largely did not include dosages or delivery mechanism. This resulted in 130,273 total (18,924 unique) drug name entries. As illustrated in Figure 2, 99,538 entries (76%) of terms were perfect matches. On the other hand, the majority of the unique terms fell into category E (14,114 out of 18,924; 75%). This was to be expected as there are a number of different incorrect ways to spell a given drug name, and only one correct way.

Bottom Line: In order to maximize utility of drug data, while minimizing cost due to manual expert intervention, we have developed a generalizable approach to automatically coding medication data using RxNorm and NDF-RT and their associated application program interfaces (APIs).This approach has enabled use of drug data in combination with other complementary information for cohort identification within an i2b2-based participant registry.The method may be generalized to other projects requiring coding of medication data from free-text.

View Article: PubMed Central - PubMed

Affiliation: Duke University, Durham, NC.

ABSTRACT
The MURDOCK Study is longitudinal, large-scale epidemiological study for which participants' medication use is collected as free text. In order to maximize utility of drug data, while minimizing cost due to manual expert intervention, we have developed a generalizable approach to automatically coding medication data using RxNorm and NDF-RT and their associated application program interfaces (APIs). Of 130,273 entries, we were able to accurately map 122,523 (94%) to RxNorm concepts, and 106,135 (85%) of those drug concepts to nodes under the Drug by VA Class branch of NDF-RT. This approach has enabled use of drug data in combination with other complementary information for cohort identification within an i2b2-based participant registry. The method may be generalized to other projects requiring coding of medication data from free-text.

No MeSH data available.