Automated Vocabulary Maintenance System for the Open Access, Collaborative Consumer Health Vocabulary

Kristina M Doing-Harris, BCompSci, MA, MS, PhD; Qing Zeng-Treitler, PhD

Department of Biomedical Informatics, University of Utah, Salt Lake City, USA

Introduction •Controlled vocabularies play an important role in the development of biomedical informatics applications.

•Consumer health vocabulary (CHV), has been rising in prominence. •Controlled vocabularies require maintenance and update, due to the continuing evolution of language itself.

•In healthcare especially there is a constant stream of new names (e.g. new medications, disorders, tests) being coined in the literature.

• CHV must keep up with these changes in the language used by consumers.

Main Question

How can a consumer health vocabulary evolve with consumer language?

Schematic Diagram of the AVM system

PatientsLikeMe.comRaw text file excerpt

PatientsLikeMe : Patients Helping Patients Live Better Every Day. Secure loginJoin today!You appear to have JavaScript disabled in your browser.PatientsLikeMe relies on JavaScript and Cookies to deliver the best possible experience to you.How do I enable JavaScript?Find Patients Just Like YouI wish this site was around years ago as I lost so much time and money doing what didn't work.Multiple Sclerosis Community Member ;Find a patient like you nowCurrent Disease CommunitiesPrevalent DiseasesALS/MND

ID Ngram in_NP firstPOS_N … Frequency … freq_in_subs51 initfunction 1 1 … 2560 … 052 pageInit100 1 1 … 490 … 121453 shortlinks35 1 1 … 228 … 83

54over navigation PatientsLikeMe Share your real-world symptom

0 0 … 34 … 9428

55navigation PatientsLikeMe Share your real-world symptom

0 1 … 34 … 9394

56navigation PatientsLikeMe Share your real-world symptom experience

0 1…

34…

9546

57PatientsLikeMe Share your real-world symptom

0 1 … 34 … 9360

58PatientsLikeMe Share your real-world symptom experience

0 1 … 34 … 9478

59PatientsLikeMe Share your real-world symptom experience with

0 1 … 34 … 9614

60 Share your real-world symptom 0 1 … 42 … 27

Excerpt from n-gram database

ID Term CUI isTerm cScore Frequency inGoldStand inMedRec68 Eszopiclone C1436328 3.6203 0 31 1 1

164 Piroxicam C0031990 3.6203 0 61 0 0214 Back pain C0004604 1.894 9 9 1 0366 Adherence 0 3.6203 0 1032 1 1402 Celecoxib C0538927 3.6203 0 31 1 1403 diagnose medical conditions 0 3.3945 0 10 0 0404 using Ankle-Foot Orthosis 0 3.366 0 9 0 0405 60 mg 0 3.2662 31 31 0 0

406Type ALS Motor Neuron Disease tom Sex

0 3.3945 0 9 0 0

407 Nebulizer Treatment device 0 3.6203 9.509775004 9 1 0408 Effi cacy Reasons taken of patients 0 2.3304 0 131 0 0

409Aquatic Therapy Exercises Treatment Report

0 4.0203 11.60964047 5 0 0

410 See all patient evaluation 0 3.3945 0 20 0 0

Excerpt from potential term databaseCHV Update Wiki

www.ConsumerHealthVocab.utah.edu/AutoVocabMaint

Stage 2 (C)

Stage 2 (A & B)

Stage 1 (A,B & C)

Results

•Combined: Termhood score threshold of 3.6 for terms found in the medical records and C-value threshold of 15.

•Produced 774 candidate terms, with 237 valid terms.

•Reviewers will find 1 valid term for every 3 or 4 candidate terms.

•Better than initial n-gram list with an average of 1 valid term for every 137 candidate terms.

Summary of Conclusions

•Social network data can be used to provide a living corpus.

•It can be mined to provide new consumer health vocabulary terms.

•Using ATR and dictionary look up can produce a concise list of candidate terms.

•Allowing the consumer health vocabulary to evolve with consumer language.

Contact InformationKristia.Doing-Harris@utah.edu

AcknowledgementsNLM Training Grant No. RO1 LM07222

CHV Websitewww.ConsumerHealthVocab.org