Normalizing dietary supplement product names using the RxNorM model

Jake Vasilakes; Yadan Fan; Rubina Rizvi; Anusha Bompelli; Olivier Bodenreider; Rui Zhang

doi:10.3233/SHTI190253

Normalizing dietary supplement product names using the RxNorM model

Jake Vasilakes, Yadan Fan, Rubina Rizvi, Anusha Bompelli, Olivier Bodenreider, Rui Zhang

Pharmaceutical Care and Health Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

Original language	English (US)
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
Publisher	IOS Press
Pages	408-412
Number of pages	5
ISBN (Electronic)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190253
State	Published - Aug 21 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/Territory	France
City	Lyon
Period	8/25/19 → 8/30/19

Bibliographical note

Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press.

Keywords

Dietary supplements
Natural Language Processing
RxNorm

Access

10.3233/SHTI190253

OpenUrl availability

Full text

Cite this

Vasilakes, J., Fan, Y., Rizvi, R., Bompelli, A., Bodenreider, O., & Zhang, R. (2019). Normalizing dietary supplement product names using the RxNorM model. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 408-412). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190253

Normalizing dietary supplement product names using the RxNorM model. / Vasilakes, Jake; Fan, Yadan; Rizvi, Rubina et al.
MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 408-412 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Vasilakes, J, Fan, Y, Rizvi, R, Bompelli, A, Bodenreider, O & Zhang, R 2019, Normalizing dietary supplement product names using the RxNorM model. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 408-412, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190253

Vasilakes J, Fan Y, Rizvi R, Bompelli A, Bodenreider O, Zhang R. Normalizing dietary supplement product names using the RxNorM model. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 408-412. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190253

Vasilakes, Jake ; Fan, Yadan ; Rizvi, Rubina et al. / Normalizing dietary supplement product names using the RxNorM model. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 408-412 (Studies in Health Technology and Informatics).

@inproceedings{b03a5bd13ca6446bb0576351209a4404,

title = "Normalizing dietary supplement product names using the RxNorM model",

abstract = "The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.",

keywords = "Dietary supplements, Natural Language Processing, RxNorm",

author = "Jake Vasilakes and Yadan Fan and Rubina Rizvi and Anusha Bompelli and Olivier Bodenreider and Rui Zhang",

note = "Publisher Copyright: {\textcopyright} 2019 International Medical Informatics Association (IMIA) and IOS Press.; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",

year = "2019",

month = aug,

day = "21",

doi = "10.3233/SHTI190253",

language = "English (US)",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "408--412",

editor = "Brigitte Seroussi and Lucila Ohno-Machado and Lucila Ohno-Machado and Brigitte Seroussi",

booktitle = "MEDINFO 2019",

}

TY - GEN

T1 - Normalizing dietary supplement product names using the RxNorM model

AU - Vasilakes, Jake

AU - Fan, Yadan

AU - Rizvi, Rubina

AU - Bompelli, Anusha

AU - Bodenreider, Olivier

AU - Zhang, Rui

PY - 2019/8/21

Y1 - 2019/8/21

N2 - The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

AB - The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

KW - Dietary supplements

KW - Natural Language Processing

KW - RxNorm

UR - http://www.scopus.com/inward/record.url?scp=85071431010&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071431010&partnerID=8YFLogxK

U2 - 10.3233/SHTI190253

DO - 10.3233/SHTI190253

M3 - Conference contribution

C2 - 31437955

AN - SCOPUS:85071431010

T3 - Studies in Health Technology and Informatics

SP - 408

EP - 412

BT - MEDINFO 2019

A2 - Seroussi, Brigitte

A2 - Ohno-Machado, Lucila

A2 - Seroussi, Brigitte

PB - IOS Press

T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019

Y2 - 25 August 2019 through 30 August 2019

ER -

Normalizing dietary supplement product names using the RxNorM model

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this