JMIR MEDICAL INFORMATICS

Park et al

Original Paper

Consumers’ Use of UMLS Concepts on Social Media:
Diabetes-Related Textual Data Analysis in Blog and Social Q&A
Sites
Min Sook Park1*, PhD; Zhe He1,2*, PhD; Zhiwei Chen3, BEng; Sanghee Oh1, PhD; Jiang Bian4, PhD
1School of Information, Florida State University, Tallahassee, FL, United States

2

Institute for Successful Longevity, Florida State University, Tallahassee, FL, United States

3

Department of Computer Science, Florida State University, Tallahassee, FL, United States

4

Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, United States*these authors contributed equally

Corresponding Author:
Zhe He, PhD
School of Information
Florida State University
Louis Shores Building
142 Collegiate Loop
Tallahassee, FL, 32306
United States
Phone: 1 850 644 5775
Fax: 1 850 644 9763
Email: zhe.he@cci.fsu.edu

Abstract
Background: The widely known terminology gap between health professionals and health consumers hinders effective information
seeking for consumers.
Objective: The aim of this study was to better understand consumers’ usage of medical concepts by evaluating the coverageof concepts and semantic types of the Unified Medical Language System (UMLS) on diabetes-related postings in 2 types of social
media: blogs and social question and answer (Q&A).
Methods: We collected 2 types of social media data: (1) a total of 3711 blogs tagged with “diabetes” on Tumblr posted between
February and October 2015; and (2) a total of 58,422 questions and associated answers posted between 2009 and 2014 in the
diabetes category of Yahoo! Answers. We analyzed the datasets using a widely adopted biomedical text processing framework
Apache cTAKES and its extension YTEX. First, we applied the named entity recognition (NER) method implemented in YTEX
to identify UMLS concepts in the datasets. We then analyzed the coverage and the popularity of concepts in the UMLS source
vocabularies across the 2 datasets (ie, blogs and social Q&A). Further, we conducted a concept-level comparative coverage
analysis between SNOMED Clinical Terms (SNOMED CT) and Open-Access Collaborative Consumer Health Vocabulary (OAC
CHV)—the top 2 UMLS source vocabularies that have the most coverage on our datasets. We also analyzed the UMLS semantic
types that were frequently observed in our datasets.
Results: We identified 2415 UMLS concepts from blog postings, 6452 UMLS concepts from social Q&A questions, and 10,378
UMLS concepts from the answers. The medical concepts identified in the blogs can be covered by 56 source vocabularies in the
UMLS, while those in questions and answers can be covered by 58 source vocabularies. SNOMED CT was the dominant vocabulary
in terms of coverage across all the datasets, ranging from 84.9% to 95.9%. It was followed by OAC CHV (between 73.5% and80.0%) and Metathesaurus Names (MTH) (between 55.7% and 73.5%). All of the social media datasets shared frequent semantic
types such as “Amino Acid, Peptide, or Protein,” “Body Part, Organ, or Organ Component,” and “Disease or Syndrome.”
Conclusions: Although the 3 social media datasets vary greatly in size, they exhibited similar conceptual coverage among
UMLS source vocabularies and the identified concepts showed similar semantic type distributions. As such, concepts that areboth frequently used by consumers and also found in professional vocabularies such as SNOMED CT can be suggested to OAC
CHV to improve its coverage.

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 1
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al(JMIR Med Inform 2016;4(4):e41) doi: 10.2196/medinform.5748
KEYWORDS
controlled vocabulary; consumer health vocabulary; concept coverage

Introduction
Background
There is a widely known language gap between healthconsumers and health care professionals [1-3]. This gap mayhinder effective communication between the 2 groups [4-7];
thus, impacting consumers’ health information seeking [3,8,9]and subsequent decision making regarding their health issues[10]. To assess the gap, Roberts and Demner-Fushman [11]used a variety of natural language processing (NLP) techniques
to analyze the difference between health questions asked byconsumers and health professionals in different online question
and answer (Q&A) sites (eg, Yahoo! Answers, and WebMD).
They found that consumer questions tend to contain more
misspelled medical terms, have longer background information,and resemble open-domain language more closely than texts
written by professionals. One major aspect of the gap is thedifference in medical vocabulary used by consumers and healthprofessionals. Zeng and colleagues [12] observed that whensearching online health information, using only consumer terms
leads to poor information retrieval results. Plovnick and Zeng
[13] later reformulated consumers’ health queries withprofessional terminology and about 40% of reformulated queries
yielded better search performance.
To bridge the vocabulary gap between health professionals andconsumers, early researchers have collected and analyzed diverse
textual data generated by consumers to identify medical terms
used by consumers. Brennan and Aronson [14] used the
MetaMap tool to extract salient concepts in nursing vocabularies
from consumers’ email messages. Smith and collegues [15] also
used MetaMap to successfully identify the Unified Medical
Language System (UMLS) concepts used by consumers in theiremail messages submitted to University of Pittsburg Cancer
Institute’s Cancer Information and Referral Service. Thesestudies aimed to bridge the vocabulary gap between healthprofessionals and consumers by identifying frequently-usedconsumer health terms that are relevant in developing
consumer-oriented health information applications and linking
free text to complex clinical knowledge resources. These ad
hoc studies represent early efforts in bridging the vocabularygap.
A controlled vocabulary is “an organized arrangement of words
and phrases used to index content and/or to retrieve content
through browsing or searching[16].” In an effort to formalize
consumer vocabulary for various applications, a controlled
vocabulary called Open-Access Collaborative Consumer Health
Vocabulary (“OAC CHV,” “CHV” for short) was recently
developed as a collection of expressions and concepts that arecommonly used by ordinary health information users [17].
Moreover, CHV has been integrated in the largest medical
terminological system–the UMLS, which has mapped terms
from different source vocabularies with the same meaning into
the same concept by the United States National Library of
https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

Medicine (NLM). As such, consumer terms are connected totheir corresponding professional terms in professional
vocabularies such as SNOMED Clinical Terms (SNOMED CT).
With CHV in the UMLS, one can translate a sentence with
consumer terms to a sentence with professional terms in an
automated fashion.
Domain coverage—the extent to which a controlled vocabulary
covers the intended domain—is one of the most desired
properties for a controlled vocabulary [18]. The usability andthe overall structure of a controlled vocabulary heavily rely
upon its coverage [19]. Traditionally, controlled vocabularydevelopment takes a top-down approach, which reflects a groupof experts’ knowledge in the respective subject matter [20,21].
For the development of CHV, however, a bottom-up approach
was taken, emphasizing 2 fundamental properties: (1) CHV
should capture actual consumers’ terms and expressions thatreflect their health information needs, and (2) the expressions
should be familiar to and used by consumers [17].
To keep up with continuous evolution of medical knowledge,
CHV needs to be updated and maintained by incorporating new,consumer-provided terms and expressions [17,22-24]. Existingstudies have shown promising results in discovering consumer
terms for CHV from social media, in particular. Vydiswaran etal [7] applied a pattern-based text mining approach to identify
pairs of consumer and professional terms from Wikipedia. Hicks
et al [25] analyzed consumer messages exchanged in Twitterin order to evaluate terms related to gender identification on
intake forms. Doing-Harris and Zeng-Treitler [24] developed
a computer assisted CHV update system, which can
automatically identify prospective terms from social media.
Identifying terms used by consumers in consumer-generated
text in aggregate fashion can account for the variability of lay
health language. These terms can be used to refine and enrich
CHV [17].
Consumers, however, may also learn and use professional terms[17,24,26]. In this sense, medical terms that are familiar to
consumers and are already established in other controlled
vocabularies could be used to improve the coverage of CHV.
Term reuse is a principle and best practice inontology/terminology development as it promises to support
the semantic interoperability and to reduce engineering costs[27]. Researchers have previously developed semi-automatedmethods to facilitate systematic term reuse. He et al [28]developed a topological-pattern-based method to identify terms
from UMLS source vocabularies to enrich SNOMED CT [28,29]
and National Cancer Institute Thesaurus (NCIt) [30].
However, this method cannot be directly applied to CHV,
because it does not have hierarchical relationships (eg.
parent-child relationship) that are necessary to constructtopological patterns [28-30]. Recently, Chandar et al [31]introduced a similarity-based term recommendation methodthat represents n-grams extracted from the free-text eligibility
JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 2
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
criteria of clinical trials as a set of linguistic and contextual
features. SNOMED CT terms are clustered with K-meansclustering. The new terms are ordered by their distance to thenearest cluster centroid, representing their similarity to existing
SNOMED CT terms. This method performed well on the corpus
of free-text clinical study eligibility criteria, because they aremostly short and partial sentences written by health professionals
with fruitful medical terms and little noise. It has yet to be tested
on free-form consumer text that typically contains lengthy
sentences and lay terms.
Most previous studies concerning CHV development
concentrated on the identification of new terms used byconsumers [17,22-24]. To the best of our knowledge, no priorstudies have conducted in-depth assessment of the coverageand popularity of medical concepts in user-generated documents
on social media. In this respect, there is a need to understandconsumers’ use of terms in existing controlled vocabularies,
and to perceive if there is the potential to improve CHV byincorporating health-related concepts used by consumers thatare covered by professional vocabularies. In this study, therefore,
we performed such an analysis in order to assess consumers’
use of medical concepts on social media postings pertaining tohealth concerns and to evaluate how many popular consumer
terms have been included in the existing source vocabulariesof the UMLS [32].
In this study, we focus on diabetes, which is recognized as oneof the most important public health problems with escalating
health concerns by the World Health Organization (WHO) [33].
Diabetes caused 1.5 million deaths in 2012 alone. It is known
to cause disability and an array of serious health issues such as
hypertension, nephropathy, and stroke [34]. Global diabetes
cases skyrocketed from 108 million in 1980 to 422 million in
2014. The number of diabetes onset will likely reach 700 million
by 2025 [35]. Diabetes and its complications not only impair
population health but also impose substantial economic burdens
on patients, their family, and the society [33].
In this study, we collected diabetes-related consumer-generated
blog postings from Tumblr and diabetes-related questions andanswers from Yahoo! Answers. We carried out text mining to
identify UMLS concepts from our datasets. Thus, we formulated
the 2 research questions (RQs): (1) To what degree do theconcepts from UMLS source vocabularies cover the concepts
used by consumers describing their diabetes-related concerns
on health postings of social media, especially blogs and social
Q&A? Which concepts do or do not overlap? (2) To what degree
are the UMLS semantic types applicable to analyzing theconcepts used by consumers when describing theirdiabetes-related concerns in social media, especially blogs andsocial Q&A? Which semantic types are observed?
In the first research question, we evaluated the coverage of allof the 178 English source vocabularies of the UMLS in our 2
datasets from Tumblr and Yahoo! Answers. In the second
research question, we analyzed the semantic types of the UMLS
concepts identified in our datasets.
The current study mainly investigated the overlap between
consumer concepts from social media and professional concepts
in the UMLS. Indeed, consumers often proactively seek andhttps://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

Park et alshare online health information on social media [36,37]. Theiruse of professional terms could be sophisticated covering bothlaypersons’ expressions and medical terminologies. In fact, notonly consumers but also health care professionals have actively
participated in creating health postings in social media [38,39].
Their use of terms in social media, however, is likely to be moreconsumer/patient-centric for health education and promotion
to the public. The comparative analysis of the concept coverage
between consumers and professional vocabularies in social
media may be helpful in understanding the scale of thephenomenon. The comparison will also help yield insights into
the nature of the vocabulary gap, which will contribute to theconsistent development of the CHV. The current study, inparticular, could shed light on how much social media users
use existing terms in UMLS source vocabularies on the web.
At the same time, findings from the current study could informthe feasibility of leveraging existing UMLS source vocabularies
to enrich the CHV.

The Unified Medical Language System
The UMLS, maintained by the NLM of the National Institutesof Health, is the largest biomedical terminological system. Its
2-level structure consists of Metathesaurus and Semantic
Network. The UMLS Metathesaurus is “a large, multi-purpose,and multi-lingual thesaurus that contains millions of biomedical
and health related concepts, their synonymous names, and theirrelationships” [40]. The UMLS Metathesaurus integrates morethan 9.1 million terms from over 170 English source
vocabularies into 3.1 million medical concepts (2015AA
version). Besides English, the UMLS also contains source
vocabularies in 20 other languages. The UMLS has integrated
most of the well-designed and well-maintained medical
terminologies such as SNOMED CT, the International
Classification of Diseases 9th Revision, Clinical Modification(ICD-9-CM), NCIt, and RxNORM. SNOMED CT is the mostcomprehensive and precise clinical terminology in the world
with over 310,000 active concepts [41]. ICD-9-CM is usedprimarily to encode the diagnoses and procedures for billing
purposes [42]. RxNORM, on the other hand, normalizes namesof all clinical drugs available on the US market and their links
to many of the drug vocabularies commonly used in pharmacy
management [43]. Most significantly, the terms with the same
meaning are mapped to the same concept in the UMLS. Due toits native term mapping, the UMLS is a valuable resource forsupporting interoperability and translation in biomedicine [32].
The NLM releases a new version of the UMLS twice a year.
The UMLS semantic types represent “a set of broad subject
categories that provide a consistent categorization of all concepts
represented in the UMLS Metathesaurus” [44]. Each concept
in the UMLS is assigned 1 or more semantic types. In the
2015AA version of the UMLS, there are a total of 127 semantic
types, describing concepts at the levels of entity and event.
Entities include physical objects such as organism, anatomical
structure, and substances. Events describe activities,
phenomenon, and processes. For example, the semantic type
“Disease or Syndrome” categorizes a set of concepts in the
UMLS that indicate “a condition which alters or interferes witha normal process, state, or activity of an organism.”
JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 3
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Consumer Health Vocabularies and Their Use inConsumer-Oriented Health Applications
OAC CHV has been used in various health-related applications
to improve patients’ access to health information. Zeng et aldeveloped a translator specifically to convert texts in electronic
health records to consumer-friendly text in patient health records
by replacing UMLS terms to their corresponding OAC CHV
terms [45]. Many UMLS concepts have one to one match withOAC CHV concepts. All the OAC CHV concepts havepredefined consumer-friendly display names. Besides OAC
CHV, other proprietary consumer health vocabularies have beendeveloped. For example, Apelon has developed a CHV and has
mapped their CHV terms to corresponding clinical concepts in
SNOMED RT (an earlier version of SNOMED CT, developed
by College of American Pathologists), ICD-9-CM, andPhysician’s Current Procedural Terminology (CPT)administrative codes. The CHV of Apelon has been used invarious applications, such as consumer health data entry, patientresults reporting clinical note translation, and Web-based
information retrieval [46]. Mayo Clinic also developed theirown consumer health vocabulary, which has a rich content ofdisease concepts as well as genetic and non-genetic risk factors
to diseases [8]. In this paper, we used OAC CHV because it is
the only publicly available consumer health vocabulary that wehave access to (through the UMLS).

Methods
Data Collection
2 types of social media were analyzed in the current study,
namely blogs and social Q&A, as they allow consumers togenerate and freely exchange health information in text format.
Health-related blogs are one of the most popular social media
venues for health information distribution. Bloggers typicallydescribe their personal experiences with diseases along with
their encounters with health care professionals [47]. Health care
professionals also create blogs for sharing their medical
knowledge and information with patients [48]. Blogs have also
been widely used for health promotion and education as acollaborative tool for both consumers and health care
professionals [49-51]. On the other hand, social Q&A is an
online community-based Q&A service where people gain
knowledge through raising questions and receiving answers
from others who willingly share their knowledge, experiences,and opinions regarding a wide range of topics including health.
Social Q&A is considered to be a knowledge-shaping sphere
for laypeople [52]. Consumers are motivated to use social Q&A
because their searches on web search engines with short queries
that are not fully expressive often fail in retrieving useful
information for their specific problems, while social Q&A
allows them to ask questions in natural language and in full
sentences [11]. For data collection, we used 2 datasets: (1)Tumblr, a popular blogging service; and (2) Yahoo! Answers,
a social Q&A service in North America.
Tumblr and Yahoo! Answers were chosen for the current study
due to their popularity and the convenience of using their
Application Program Interfaces (APIs), which allowed us tocollect data automatically from these sites. Also, both Tumblr
https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

Park et al
and Yahoo! Answers do not limit the number of words inpostings. As such, their users can elaborate their health concerns
and information on postings with sufficient details, thereby
providing us ample opportunities to extract and analyze relevant
concepts from the postings.
Tumblr is one of the fastest-growing blog sites with nearlytwenty-fold increase in the number of blogs from October 2012
to October 2015 [53]. It launched relatively late in the market
compared to other sites such as WordPress and Blogger, but isrecognized as one of the best blog sites due to its ease of setup,stylish interface design, and micro-blogging support [54,55]. It
has over 227 million blogs and 37 million unique visitors as ofFebruary 2016 [53]. From Tumblr, we collected a total of 3711
English text blogs with a tag related to “diabetes” (eg,“diabetes,” “diabetes mellitus,” and “Type 2 diabetes”) posted
between February and October 2015.
Yahoo! Answers is one of the most popular social Q&A sites
with approximately 5.6 million visitors per month as of February
2016 [56]. From Yahoo! Answers, we garnered a total of 58,422
questions and associated answers between 2009 and 2014 in
the diabetes category of Yahoo! Answers. During data analysis,
we carried out text mining with questions and answers(specifically, best answers) separately, because the informationin questions and answers could be different. Questions could
capture health concerns and associated problems, while answers
could mainly discuss information resources intended to solve
the problems. It is important to note that 1 question may havemore than one answer. In this study, we limited answers to theone selected as the best answer by the questioner. The datacollected from Yahoo! Answers were separated into questions
and answers in the subsequent analyses.

Units of Analysis
Once we collected text data from Tumblr and Yahoo! Answers,
we mined the text data for “concepts,” a unit of understanding
which represents a fundamental component of terminology [57]or unit of meaning in an ontology [31]. Concepts are different
from “terms” in that a term refers to an entity or “physicalobject” written or spoken in text to represent a concept or
thought [58]. In the UMLS, a term is described as a “word orcollection of words comprising an expression,” which indicatesa class of all lexical variants (eg, “eye,” “Eye,” “eyes”) [59].
The UMLS assigned each term an atom unique identifier (AUI)
and grouped the terms with the same meaning into a concept
with a concept unique identifier (CUI). We also analyzed thesemantic types of the extracted concepts in order to understandthe broad semantic categories of the terms that are frequently
used by consumers.

Textual Data Processing
We used a widely adopted biomedical text processing
framework Apache cTAKES™ [60] and its extension YTEX[61] to identify UMLS terms in our datasets. Apache cTAKES
is designed as a natural language processing (NLP) system forextraction of information from the free-text data available inelectronic medical records (EMRs). It provides an agile andflexible platform based on the Unstructured Information
Management Architecture (UIMA) and a rich NLP library.
JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 4
(page number not for citation purposes)

JMIR MEDICAL INFORMATICSYTEX, a module of cTAKES, provides Word Sense
Disambiguation (WSD), data mining and feature engineeringfunctionalities. We mainly used the WSD function of YTEX torecognize the most possible UMLS concept when a term in thefree text can be matched to multiple ambiguous concepts. We
used the 3.2.2 release of cTAKES and YTEX with the default
workflow configuration named “Aggregate Plaintext UMLS
Processor.”
Figure 1 illustrates our overall analysis process. First, eachdocument is a blog posting from Tumblr, a question or ananswer from Yahoo! Answers. Each blog posting may consistof 1 or more sentences. Then, cTAKES detected and split eachdocument into individual sentences using the sentence detectorof OpenNLP [62,63], with the default configuration for English
text. For each sentence, cTAKES performed tokenization withthe default tokenizer of the OpenNLP, lexical variant generation
using the lexical tool provided by the United States National
Library of Medicine with the default configuration. Then,

Park et al
cTAKES performed Part-Of-Speech (POS) tagging using the
POS tagger in OpenNLP with the information entropy-basedmodel for English to generate the candidate terms for further
processing. Then, YTEX matched the candidate terms with all
the possible UMLS terms, which were preloaded from the
MRCONSO table of the UMLS 2015AA release. We then stored
the matching results to a MySQL database. For each candidate
term, there may be 0, 1, or more matching UMLS terms withdifferent semantics. To identify terms with reasonable semantics,
we used YTEX to perform word sense disambiguation (WSD),
in which the intrinsic information content (IC) measure is usedas the semantic similarity metric with a window of 50 words as
the context for WSD. The intrinsic information content is ameasure of concept specificity computed from the structure of
the taxonomy in a biomedical terminology and does not rely on
the term frequency in the corpus. The details of the intrinsic IC
measure can be found in Garla et al [64]. Finally, all the UMLS
terms in each record were extracted with a UMLS CUI.

Figure 1. Conceptual framework of the study. Dots refer to concepts extracted from the dataset and gray dots refer to concepts mapped to the concepts
in one of the UMLS source vocabularies.

Concept Coverage Analysis
We first analyzed the basic characteristics of the overall concept
coverage across our datasets collected from Tumblr and Yahoo!
Answers: (1) blog postings from Tumblr, (2) questions inYahoo! Answers, and (3) answers in Yahoo! Answers. We thenanalyzed the coverage of each source vocabulary in the UMLS
across the datasets. SNOMED CT and CHV are the 2
vocabularies with the highest concept coverage in our datasets.
Thus, we conducted a concept coverage analysis of SNOMED
CT and CHV based on our datasets. We also analyzed thesemantic types of the concepts identified from our datasets.

Results

from answers. Table 1 shows the total number of documents
and sentences in our datasets (ie, blog postings, questions,answers). These numbers were compared to the “# with UMLS
concepts,” the unique number of documents and sentences
containing the identified UMLS concepts. Note that we canonly extract concepts that are presented in UMLS. Thus, thetotal number of concepts in our datasets (which can include
concepts that are not in UMLS) is not provided in Table 1.
There was a noticeable variation across the datasets. Over 80%of the documents from questions and answers contained 1 ormore UMLS concepts whereas less than half of the documentsfrom blogs did. Over half of the sentences from questions andanswers contained at least 1 UMLS concept, while only 27%of those from blog posts contained at least 1 UMLS concept.

Aggregate Characteristics of the Datasets
We identified 2415 UMLS concepts from blog postings, 6452
UMLS concepts from questions, and 10,378 UMLS concepts

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 5
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al

Table 1. Basic characteristics of UMLS concept coverage in our datasets.
TumblrYahoo! Answers

Blog postings

Questions

Answers

Total #

# with UMLS concepts

Total #

# with UMLS concepts

Total #

# with UMLS concepts

Document

3711

1388 (37.4%)

58,42251,850 (88.8%)

58,42251,550 (88.2%)

Sentence

47,41312,802 (27.0%)

249,013

142,802 (57.3%)

348,793

216,736 (62.1%)

Concepts

–

2415

–

6452

–

10,378Coverage by the UMLS Source Vocabularies
The concepts in the blogs were covered by 56 UMLS source
vocabularies, while those in questions and answers were covered
by 58 source vocabularies. Table 2 illustrates the top 20 most
covered UMLS source vocabularies (The full names and theversion information of the source vocabularies can be found in
the Multimedia Appendix 1 Table-A1). SNOMED CT was
dominant across all our datasets, ranging from 84.9% to 95.9%.
It was followed by CHV (between 73.5% and 80.0%) and MTH(between 55.7% and 73.5%). Other source vocabularies within
the top 10 for all of our datasets are: NCIt, Medical Subject
Headings (MeSH), Computer Retrieval of Information on
Scientific Projects Thesaurus (CSP), Library of Congress
Subject Headings Northwestern University subset (LCH NW),
Logical Observation Identifier Names and Codes (LOINC), andNational Drug File – Reference Terminology (NDFRT),
although the ranking order varies slightly across different
datasets. Multimedia Appendix 1 Table-A2 provides example
concepts in the top 3 most covered source vocabularies.

Table 2. Top 20 mostly covered UMLS source vocabularies.

Rank

TumblrYahoo! Answers

Blogs (n=2415)

Questions (n=6452)

Answers (n=10,378)

Source vocabulary# of concepts%

Source vocabulary# of concepts%

Source vocabulary# of concepts%

1

SNOMED CT

2315

95.9

SNOMED CT

5476

84.9

SNOMED CT

9032

87.0

2

CHV

1931

80.0

CHV

4928

76.4

CHV

7625

73.5

3

MTH

1774

73.5

MTH

3899

60.4

MTH

5780

55.7

4

NCIt

1156

47.9

MeSH

2957

45.8

MeSH

4796

46.2

5

MeSH

1130

46.8

NCIt

2917

45.2

NCIt

4485

43.2

6

CSP

812

33.6

CSP

1840

28.5

NDFRT

2999

28.9

7

AOD

775

32.1

NDFRT

1775

27.5

CSP

2839

27.4

8

LCH_NW771

31.9

LCH_NW

1627

25.2

LCH_NW

2436

23.5

9

LOINC

697

28.9

AOD

1585

24.6

AOD

2335

22.5

10

NDFRT

659

27.3

LOINC

1510

23.4

RXNORM

2099

20.2

11

LCH

587

24.3

RXNORM

1421

22.0

LOINC

2081

20.1

12

NCI_NCI-GLOSS

475

19.7

LCH

1187

18.4

LCH

1730

16.7

13

MEDLINEPLUS

402

16.6

NCI_NCI-GLOSS

952

14.8

NCI_FDA

1387

13.4

14

CST

365

15.1

NCI_FDA

868

13.5

DXP

1322

12.7

15

COSTAR

362

15.0

COSTAR

835

12.9

NCI_NCI-GLOSS

1321

12.7

16

NCI_FDA

345

14.3

DXP

830

12.9

COSTAR

1257

12.1

17

OMIM

342

14.2

CST

794

12.3

OMIM

1234

11.9

18

RXNORM

338

14.0

OMIM

790

12.2

CST

1206

11.6

19

DXP

326

13.5

MEDLINEPLUS

721

11.2

VANDF

1117

10.8

20

ICD9CM

241

10.0

VANDF

644

10.0

MTHSPL1033

10.0

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 6
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al

Table 3. Top 10 frequently observed concepts covered by both SNOMED CT and CHV.
Rank

TumblrYahoo! Answers
Questions

Answers

Concept

Freq.

Concept

Freq.

Concept

Freq.

1

Blood (C0005767)

816

Blood (C0005767)

30,654

Blood (C0005767)

54,6892

Pain (C0030193)

798

Sugars (C0242209)

29,593

Sugars (C0242209)

49,207

3

Insulin (C0021641)

744

Insulin (C0021641)

10,816

Insulin (C0021641)

27,8874

Pharmaceutical preparations(C0013227)

719

Glucose (C0017725)

7394

Glucose (C0017725)

26,4205

Sugars (C0242209)

699

Problem (C0033213)

5111

Pharmaceutical preparations(C0013227)

11,571

6Disease (C0012634)

617

Water (C0043047)

4781

Diseases (C0012634)

9733

7

Problem (C0033213)

568

Pharmaceutical preparations(C0013227)

4456

Carbohydrates (C0007004)

9517

8

Diabetes mellitus (C0011849)

501

Hematologic tests (C0018941)

3784

Problem (C0033213)

9248

9

Teeth structure (C0040426)

424

Pain (C0030193)

3625

Water (C0043047)

5994

10

Operative surgery procedures(C0543467)

375

Urine (C0042036)

2550

Fasting (C0015663)

5848

Table 4. Top 10 frequently observed concepts covered by CHV but not SNOMED CT.
TumblrYahoo! Answers

Rank

a

Questions

Answers

Concept (CUI)aFreq.

Concept (CUI)

Freq.

Concept (CUI)

Freq.

1

Cider vinegar (C0937941)

54

Stomach (C0038351)

1050

Lantus (C0876064)

689

2Apple cider vinegar (C1178459)

54

Lantus (C0876064)

571

Actos (C0875954)

659

3

Lantus (C0876064)

15

Humalog (C0528249)

260

Avandia (C0875967)

628

4

Gentle (C0720654)

11

NovoLog (C0939412)

180

HumaLog (C0528249)

289

5

Corrective (C0719519)

9

Glucophage (C0591573)

131

NovoLog (C0939412)

255

6

Botox (C0700702)

9

Levemir (C1314782)

122

Levemir (C1314782)

184

7

RID (C0073361)

6

Actos (C0875954)

95

Glucophage (C1314782)

161

8

HumaLog (C0528249)

5

Seroquel (C0287163)

78

Novolin (C0028467)

112

9Bead Dosage Form (C0991566)

3

Synthroid (C0728762)

62

Viagra (C0663448)

105

10

Actos (C0875954)

3

Coumadin (C0699129)

54

Triphosphat (C0146894)

77

CUI: concept unique identifier

There was significant overlap between the concepts from the
top 2 source vocabularies, SNOMED CT and CHV 78.2%(1889/2415) concepts from blog postings, 70.0% (4518/6452)
concepts in questions, and 68.4% (7095/10,378) concepts in
answers. Table 3 shows the top 10 concepts. Note that we only
show the preferred term of the concept in the UMLS throughout
the paper. Diabetes-related concepts such as Blood, Sugars,
Insulin, Glucose, and Diabetes mellitus were frequently
mentioned (preferred names of a UMLS concept are denoted
in italics). At the same time, it includes some general medical
concepts such as disease, pharmaceutical preparations, andproblem. Concepts related to glucose level in blood such as
blood, sugars, glucose, and carbohydrates also appeared withhigh frequency.
https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

A few concepts were only covered by CHV: 1.7% (40/2415)
concepts in blog postings, 6.3% (409/6452) concepts inquestions, and 5.1% (529/10,378) concepts in answers. Table
4 shows the top 10 most frequently observed UMLS concepts
covered by CHV but not SNOMED CT in our datasets.
All the concepts in Table 4 are about pharmacologicalsubstances or organic chemicals, except stomach found withinquestions. Three concepts regarding insulin therapy for diabetes,
such as Lantus (ie, insulin glargine injection), Humalog (ie,insulin lispro injection), and Actos (ie, pioglitazonehydrochloride) in blog postings and questions/answers appearedwith high frequency. Diabetes-treatment-related concepts, suchas NovoLog and Glucophage, are more frequently observed inquestions and answers than blog postings. In total, 9 out of the
JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 7
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al

top 10 concepts in questions and answers were diabetes
medications. Only 2 concepts, namely stomach in questions andViagra in answers, are not directly related to diabetes treatment.
On the other hand, some concepts in blogs were indirectly
related to diabetes. For example, cider vinegar, apple cider
vinegar, and Botox also frequently appeared.
There were also the concepts covered by SNOMED CT but not
CHV: 17.6% (424/2415) concepts from blog postings, 957/6452(14.8%) concepts in questions and 18.7% (1936/10,378)
concepts in answers (See Table 5). Human body related

concepts, such as back structure excluding neck, entire heart,
entire pancreas, entire kidney, entire skin, and entire eye, were
frequently used to describe their diabetes issues in blog postingsor questions/answers. Three concepts, entire skin, symptoms
and fatty acid glycerol esters were observed from all ourdatasets. Massage and training were frequently mentioned inblog postings, while injection procedure and protective cup
were frequently mentioned in questions and answers but were
not mentioned as frequently as in blog postings. As theseconcepts were frequently observed in social media, CHV should
consider importing them to enrich its conceptual content.

Table 5. Top 10 frequently observed concepts covered by SNOMED CT but not CHV.
TumblrYahoo! Answers

Rank

a

Questions

Answers

Concept (CUI)aFreq.

Concept (CUI)

Freq.

Concept (CUI)

Freq.

1

Entire skin (C1278993)

524

Symptoms (C1457887)

7690

Symptoms (C1457887)

12,7272

Symptoms (C1457887)

393

Fatty acid glycerol esters(C0015677)

1789

Fatty acid glycerol esters(C0015677)

8727

3

Back structure, excluding neck
(C1995000)

236

Entire foot (C1281587)

1647

Entire cells (C1269647)

6435

4

Massage (C0024875)

217

Back structure, excluding neck
(C1995000)

1589

Entire heart (C1281570)

3204

5

Fatty acid glycerol esters (C0015677) 210

Entire kidney (C1278978)

1368

Entire pancreas (C1278931)

3003

6

Training (C0220931)

163

Entire eye (C1280202)

1210

Entire skin (C1278993)

2614

7

Entire pancreas (C1278931)

157

Protective cup (C1533124)

1159

Protective cup (C1533124)

2178

8

Entire heart (C1281570)

156

Entire lower limb (C1269079)

985

Entire stomach (C1278920)

1876

9

Entire oral cavity (C1278910)

138

Entire hands (C1281583)

969

Injection procedure(C1533685)

1561

10

Entire spine (C1280065)

137

Entire skin (C1278993)

912

Entire bony skeleton(C1266909)

1501

CUI: concept unique identifier

Semantic Types of the Identified Concepts
Among 127 UMLS semantic types (STY), about half of themwere identified in our datasets: 52 STYs (40.9%) in the blog
postings, 59 STYs (46.5%) in the questions, and 54 STYs
(42.5%) in the answers. In general, there was a significant
overlap of STYs across our datasets with 52 shared STYs. Seven
STYs, however, were identified in the questions only, including“Functional Concept,” “Intellectual Product,” “Laboratory
Procedure,” “Organ or Tissue Function,” “Organism Attribute,”“Social Behavior,” and “Substance.” Two STYs, “Fully Formed
Anatomical Structure” and “Cell or Molecular Dysfunction,”were not found in questions, but in both the answer dataset andthe blog dataset. Table 6 shows the top 20 frequent semantic
types of the identified UMLS concepts in the different datasets
respectively.
When comparing the top 10 frequently observed STYs acrossthe datasets, 9 out of 10 STYs (ie, “Finding,” “Pharmacologic

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

Substance,” “Therapeutic or Preventive Procedure,” “Disease
or Syndrome,” “Organic Chemical,” “Body Part, Organ, orOrgan Component,” “Sign or Symptom,” “Medical Device,”and “Amino Acid, Peptide, or Protein”) commonly appeared
across the datasets with minor differences in terms of frequency.
“Laboratory Procedure” appeared frequently in questions but
not in blogs and answers. “Pathologic Function” appeared
frequently in answers but not in blogs and questions. Example
concepts of the frequently observed STYs showed that laypeople
tend to frequently use common concepts to describe theirdiabetes-related issues in social media. To illustrate, Sugars,
Insulin, Glucose ranked in top 5 concepts of the STY“Pharmacologic Substance.” Similarly, the concepts such as
Disease and Communicable Diseases appeared frequently
among the concepts of the STY “Disease or Syndrome.” Weprovide the top 5 frequent concepts for the top 10 frequently
observed semantic types in Multimedia Appendix 1 Table A3.

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 8
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al

Table 6. Top 20 frequently observed semantic types of the identified concepts.
Rank

TumblrYahoo! Answers

Blogs

Questions

Semantic type1

Finding

Concepta

Semantic type

n (%)

Freq.

380

5277

(15.7)
2

3

4

Pharmacologic substance307(12.7)

4413

Therapeutic or preventive procedure

241

3184

7

8

2923

10

Concept
n (%)

Freq.
185,880

Pharmacologic substance

1240 53,976(19.2)

Pharmacologic substance1995

Organic chemical

1006

Organic chemical

1692

41,255

Finding895(19.2)
123,509(16.3)
30,458

Disease or syndrome 1511

57,379(14.6)

Disease or syndrome 743
28,041(11.5)

Finding2737

Body part, organ, or 484
organ component(7.5)

27,172

Body part, organ, or 666
organ component(6.4)

48,584

Body part, organ, or 208
organ component
(8.6)

2566

Sign or symptom

19,601

Therapeutic or preventive procedure

16,555Sign or symptom

145
(6.0)

2214

134

1319

Organic chemical

225

Medical device

Amino acid, peptide, 70
or protein
(2.9)

1112

Biologically active
substance

1093

69
(2.9)

338
(5.2)

(5.5)
9

Freq.

(13.9)(9.3)
6

Semantic type(15.6)(9.9)
5

Conceptn(%)(10.0)

Disease or syndrome 239

Answers

Therapeutic or preventive procedure

331

1302

76,765(12.5)

583(5.6)

16,372

Amino acid, peptide, 495
or protein(4.8)

40,521

Amino acid, peptide, 305
or protein(4.7)

13,178Sign or symptom

38,905

Medical device

12,862

(5.1)

201

(4.2)
Medical device(3.1)
Laboratory procedure

180
(2.8)

436

347

20,391(3.3)
10,580Pathologic function

292

12,551(2.8)

a

The percentage was calculated based on the total number of unique identified UMLS concepts: blogs in Tumblr: n=2415, questions in Yahoo! Answers:
n=6452, answers in Yahoo! Answers: n=10,378

Discussion
Principal Findings
Previous studies [12-15] utilized user-generated documents
including social media. However, they mainly used a single test
bed based on the assumption that the selected test bed would
properly reflect people’s medical concepts. Our study involved
different types of social media which contains texts thatlaypeople generated for different purposes: questions are forexpressing their health information seeking needs; blogs andanswers are more likely for sharing their knowledge,
experiences, and opinions to others. The current study
investigated the terminology coverage in consumer-generated
text in social media by identifying UMLS concepts and theirsemantic types. Our findings demonstrated that consumers use
medical concepts not only from controlled vocabularies
developed for consumers (ie, CHV) but also those for healthprofessionals (eg, SNOMED CT). Our results are in line withprior observations that consumers use both lay and professionalterms [24,26,65] and demonstrated that CHV can be enrichedby other source vocabularies in the UMLS.
The UMLS concept usage in blogs and social Q&A wasdifferent in that the UMLS concepts appeared more frequently
https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderXin the postings of social Q&A (almost 90% questions andanswers) in comparison to blog postings (about 30%). Social
Q&A users mainly discuss health-related issues (in the current
study, diabetes-related issues) in their postings, because their
participation in question asking and answering is purpose-driven.
On the other hand, blog users often elaborate nonhealth related
topics in their postings, although they tagged their postings with“diabetes.”
In spite of the differences of the overall UMLS concept coverage
between blogs and social Q&A, we found that the UMLS
concepts identified in different datasets can be covered by asimilar number of UMLS source vocabularies. Two UMLS
source vocabularies, ie, SNOMED CT and CHV, showed thebest coverage. Social media users in our datasets may haveadvanced medical knowledge because they often use
professional terms. CHV demonstrated the second largest
coverage for all the datasets despite the fact that CHV has amuch smaller number of concepts and terms than SNOMED
CT (1:6 ratio). CHV was developed to incorporate consumer
expressions presented in consumer-generated text data. Ourfindings showed that different social media platforms may playa similar role as consumer-generated documents for CHV
enrichment, which confirmed the literature [66].
JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 9
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
A comparison of the concept coverage between SNOMED CT
and CHV in our datasets led us to examine the difference
between the concept usages among blog and social media users.
For example, cider vinegar, apple cider vinegar, massages, andtraining were frequently mentioned in blog postings, while they
were not frequently mentioned in questions and answers.
However, concepts pertaining to insulin therapy, such as Lantus,
Humalog, and Actos, are frequently used in questions/answers.
Consumers often inquire about a variety of insulin therapies insocial Q&A, while blogs often include recipes specific to theuse of vinegar, a popular ingredient in diabetes-controlling food.
Botox and Viagra were often mentioned in blog postings andanswers. They could be important for diabetic patients, although
they may not be closely related to control diabetes. It would beinteresting to further analyze the relationship of these terms todiabetes. An in-depth analysis of the identified concepts alongwith how they are used in the original postings could produce
useful information for understanding consumers’ informationneeds and use.
According to our analysis, the percentage of unique concepts
covered by CHV but not by SNOMED CT varied from 1.7%
to 6.3%. In the blog dataset, where approximately 3000 blogs
were analyzed, only 40 concepts were covered by CHV
exclusively. On the other hand, in Yahoo! Answers, 409
concepts (6.3%) in questions and 529 concepts (5.1%) in
answers were covered by CHV but not by SNOMED CT. These
results indicate that the larger datasets would yield more lay
concepts. The size of dataset also appeared to affect the diversityof semantics. The same set of 9 semantic types was observed
frequently in all our datasets. “Finding,” “Pharmacologic
Substance,” and “Disease and Syndrome” were among the top
4 most observed semantic types.
Differences were observed as well. Blogs might be better
platforms for consumers to discuss organic chemical,
pharmacologic substances, or therapeutic or preventive
procedure for diabetes. Yet, concepts of organic chemical andpharmacologic substances were also frequently used in social
Q&A. In social Q&A data, 7 semantic types that were not
identified in blogs were observed, indicating that larger datasets
may yield more diverse medical concepts.

Limitations
This study has a few limitations. First, the blog data in Tumblr
and Yahoo! Answers data were collected in different time frames
and are different in size, which might have affected the findingsof this study. Smaller volumes of blog data used in this study
may affect the diversity of the UMLS concepts identified. Even
though blogging and question posting/answering are dynamic
online activities for those living with chronic diseases, Tumblr

Park et al
and Yahoo! Answers may not represent all the healthinformation users’ concept usage. The datasets could be
expanded to include other types of social media such as
diabetes-related discussion boards. The users of these online
sources may be biased towards those with greater technological
proficiency, such as those who are younger, more educated or
those in a higher socioeconomic status who are more likely toseek health information on the Internet. This study may notreflect the experiences of those who are older adults, lesseducated or underprivileged [67]. Second, even though theautomated NLP techniques that were employed in the current
study were cost-effective, direct interaction with ordinary healthinformation users would allow the researchers to capture moreaccurate meaning of medical concepts that these individuals
commonly use to describe their health issues. Moreover, aqualitative approach such as content analysis also would help
to identify contextual semantics of the concepts. Third, although
the WSD function of YTEX is effective in selecting the mostpossible UMLS concepts for a term in free text, the same term
may be matched to different ambiguous UMLS concepts. Thisis mainly due to the fact that the UMLS may contain unmappedsynonymous concepts. Ideally, manual review by domain experts
could be applied to further refine the automatic mapping results.

Conclusions
The current study examined the potential of social media as
user-generated documents in which consumers’ medical
concepts can be observed and leveraged for controlled
vocabulary development for ordinary health information users.
We selected and tested 2 social media venues, namely blogs
and social Q&A. Our findings showed stronger similarities
rather than differences in the controlled vocabulary usage. The
size of a dataset may affect the number of concepts identified.
However, the similarities in the source vocabularies, frequently
used concepts, and semantic types of the concepts indicate thatsocial media sites tend to reflect the common sense of laypeople.
More importantly, we found that social media users not only
employ consumer concepts in CHV but also concepts inprofessional vocabularies such as SNOMED CT. This indicatesthat CHV still has room for improvements by incorporating
concepts from other UMLS source vocabularies. The focus of
our study is not to identify a list of consumer medical concepts,
but to test the feasibility of leveraging social media data toidentify consumer concepts covered by existing UMLS source
vocabularies. Ultimately, it would assist consumers’ healthinformation searches online, narrowing the disparity between
ordinary health information users and medical professionals.
In future studies, we will employ automated approaches to
identify and recommend new medical terms and concepts fromsocial media to enrich CHV.

Acknowledgments
We would like to thank Dr Warren Allen for providing the computing resources for this work. This work was partially supported
by an Amazon Web Services Education and Research Grant Award (PI: He). The work was also partially supported by National
Center for Advancing Translational Sciences under the Clinical and Translational Science Award UL1TR001427 (PI: Nelson &Shenkman). The content is solely the responsibility of the authors and does not represent the official view of the National Institutesof Health.

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 10(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al

Authors' Contributions
MP initiated the idea of this study. ZH led the conceptualization, design, and implementation of this study. MP collected andprovided the blog data from Tumblr.com. SO collected and provided the social Q&A data from Yahoo! Answers. ZC performed
the natural language processing on the datasets and structured the results in a relational database. MP performed the data analysis
and drafted the initial version; ZH, SO, BJ extensively revised the draft critically and iteratively for important intellectual content.
All authors contributed to the methodology development, results interpretation, edited the paper significantly, and gave final
approval for the version to be published. ZH takes primary responsibility for the research reported here.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Table A1. Full names of the UMLS source vocabularies in Table 2. Table A2. Top 10 frequently observed concepts in the top 3
most covered source vocabularies. Table A3. Top 5 most frequently concepts in the top 9 frequent semantic types.
[PDF File (Adobe PDF File), 125KB-Multimedia Appendix 1]

References
1.
2.
3.
4.

5.
6.
7.
8.

9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.

Messai R, Simonet M, Bricon-Souf N, Mousseau M. Characterizing consumer health terminology in the breast cancer field.
Stud Health Technol Inform 2010;160(Pt 2):991-994. [Medline: 20841832]
Poikonen T, Vakkari P. Lay persons? and professionals? nutrition-related vocabularies and their matching to a general anda specific thesaurus. Journal of Information Science 2009;35(2):232-243.
Smith CA, Wicks PJ. PatientsLikeMe: Consumer health vocabulary as a folksonomy. AMIA Annu Symp Proc 2008:682-686[FREE Full text] [Medline: 18999004]
Patrick TB, Monga HK, Sievert ME, Houston HJ, Longo DR. Evaluation of controlled vocabulary resources for developmentof a consumer entry vocabulary for diabetes. J Med Internet Res 2001;3(3):E24 [FREE Full text] [doi: 10.2196/jmir.3.3.e24][Medline: 11720966]
Zielstorff RD. Controlled vocabularies for consumer health. J Biomed Inform 2003;36(4-5):326-333 [FREE Full text][Medline: 14643728]
Tse T, Soergel D. Exploring medical expressions used by consumers and the media: an emerging view of consumer healthvocabularies. AMIA Annu Symp Proc 2003:674-678 [FREE Full text] [Medline: 14728258]
Vydiswaran VG, Mei Q, Hanauer DA, Zheng K. Mining consumer health vocabulary from community-generated text.
AMIA Annu Symp Proc 2014;2014:1150-1159 [FREE Full text] [Medline: 25954426]
Seedorff M, Peterson KJ, Nelsen LA, Cocos C, McCormick JB, Chute CG, et al. Incorporating expert terminology anddisease risk factors into consumer health vocabularies. Pac Symp Biocomput 2013:421-432 [FREE Full text] [Medline:
23424146]
Gross T, Taylor A. What have we got to lose? The effect of controlled vocabulary on keyword searching results. College& Research Libraries 2005;66(3):212-230.
Lewis D, Eysenbach G, Jimison H, Kukafka R, Stavri P. Consumer health informatics. In: Consumer Health Informatics:
Informing Consumers and Improving Health Care. New York, NY: Springer; 2005:1-7.
Roberts K, Demner-Fushman D. Interactive use of online health resources: a comparison of consumer and professionalquestions. J Am Med Inform Assoc 2016 Jul;23(4):802-811. [doi: 10.1093/jamia/ocw024] [Medline: 27147494]
Zeng Q, Kogan S, Ash N, Greenes RA. Patient and clinician vocabulary: how different are they? Stud Health Technol
Inform 2001;84(Pt 1):399-403. [Medline: 11604772]
Plovnick RM, Zeng QT. Reformulation of consumer health queries with professional terminology: a pilot study. J MedInternet Res 2004 Sep 03;6(3):e27 [FREE Full text] [doi: 10.2196/jmir.6.3.e27] [Medline: 15471753]
Brennan PF, Aronson AR. Towards linking patients and clinical information: detecting UMLS concepts in e-mail. J Biomed
Inform 2003;36(4-5):334-341. [Medline: 14643729]
Smith CA, Stavri PZ, Chapman WW. In their own words? A terminological analysis of e-mail to a cancer information
service. Proc AMIA Symp 2002:697-701 [FREE Full text] [Medline: 12463914]
Harpring P. What Are Controlled Vocabularies? In: Baca M, editor. Introduction to Controlled Vocabularies: Terminology
for Art, Architecture, and Other Cultural Works. Los Angeles, CA: Getty Publications; 2010.
Zeng QT, Tse T. Exploring and developing consumer health vocabularies. J Am Med Inform Assoc 2006;13(1):24-29[FREE Full text] [doi: 10.1197/jamia.M1761] [Medline: 16221948]
Cimino JJ. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med 1998
Nov;37(4-5):394-403 [FREE Full text] [Medline: 9865037]
Arts DG, Cornet R, de Jonge E, de Keizer NF. Methods for evaluation of medical terminological systems--a literature
review and a case study. Methods Inf Med 2005;44(5):616-625. [Medline: 16400369]

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 11(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
20.
21.

22.
23.
24.
25.

26.
27.
28.

29.

30.
31.
32.
33.
34.
35.
36.
37.

38.
39.
40.
41.
42.
43.

44.
45.
46.

Greenberg J. Metadata and the World Wide Web. In: Drake M, editor. Encyclopedia of Library and Information Science.
New York, NY: Marcel Deker, Inc; 2003:1876-1888.
Mathes A. Folksonomies - cooperative classification and communication through shared metadata. 2004 Dec. URL: http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html [accessed 2016-11-02] [WebCite
Cache ID 6lilnyrVJ]
Kim S. An exploratory study of user-centered indexing of published biomedical images. J Med Libr Assoc 2013
Jan;101(1):73-76 [FREE Full text] [doi: 10.3163/1536-5050.101.1.011] [Medline: 23405049]
MacLean DL, Heer J. Identifying medical terms in patient-authored text: a crowdsourcing-based approach. J Am Med
Inform Assoc 2013;20(6):1120-1127 [FREE Full text] [doi: 10.1136/amiajnl-2012-001110] [Medline: 23645553]
Doing-Harris KM, Zeng-Treitler Q. Computer-assisted update of a consumer health vocabulary through mining of social
network data. J Med Internet Res 2011 May 17;13(2):e37 [FREE Full text] [doi: 10.2196/jmir.1636] [Medline: 21586386]
Hicks A, Hogan WR, Rutherford M, Malin B, Xie M, Fellbaum C, et al. Mining Twitter as a First Step toward Assessing
the Adequacy of Gender Identification Terms on Intake Forms. AMIA Annu Symp Proc 2015;2015:611-620 [FREE Fulltext] [Medline: 26958196]
Lewis D, Brennan PF, McCray AT, Tuttle M, Bachman J. If We Build It, They Will Cometandardized Consumer
Vocabularies. Studies in Health Technology and Informatics 2001;84:1530 [FREE Full text]
Kamdar MR, Tudorache T, Musen M. A Systematic Analysis of Term Reuse and Term Overlap across Biomedical Ontologies.
Semantic Web – Interoperability, Usability, Applicability 2016 (forthcoming). [FREE Full text]
He Z, Geller J, Chen Y. A comparative analysis of the density of the SNOMED CT conceptual content for semantic
harmonization. Artif Intell Med 2015 May;64(1):29-40 [FREE Full text] [doi: 10.1016/j.artmed.2015.03.002] [Medline:
25890688]
He Z, Geller J, Elhanan G. Categorizing the Relationships between Structurally Congruent Concepts from Pairs ofTerminologies for Semantic Harmonization. AMIA Jt Summits Transl Sci Proc 2014;2014:48-53 [FREE Full text] [Medline:
25717400]
He Z, Chen Y, de Coronado S, Piskorski K, Geller J. Topological-Pattern-based Recommendation of UMLS Concepts for
National Cancer Institute Thesaurus. AMIA Annu Symp Proc 2016 (forthcoming).
Chandar P, Yaman A, Hoxha J, He Z, Weng C. Similarity-Based Recommendation of New Concepts to a Terminology.
AMIA Annu Symp Proc 2015;2015:386-395 [FREE Full text] [Medline: 26958170]
Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res
2004 Jan 1;32(Database issue):D267-D270 [FREE Full text] [doi: 10.1093/nar/gkh061] [Medline: 14681409]
World Health Organization. Global Report on Diabetes. World Health Organization. URL: http://apps.who.int/iris/bitstream/
10665/204871/1/9789241565257_eng.pdf?ua=1 [accessed 2016-11-02] [WebCite Cache ID 6limIzVBr]
American Diabetes Association. Diabetes complications. URL: http://www.diabetes.org/living-with-diabetes/complications/[accessed 2016-11-02] [WebCite Cache ID 6limMPM2T]
Krug EG. Trends in diabetes: sounding the alarm. Lancet 2016 Apr 9;387(10027):1485-1486 [FREE Full text] [doi:10.1016/S0140-6736(16)30163-5] [Medline: 27061675]
Fox S. The social life of health information. Pew Research Center. URL: http://www.pewresearch.org/fact-tank/2014/01/
15/the-social-life-of-health-information/ [accessed 2016-11-02] [WebCite Cache ID 6limSyxAR]
Andersen NB, Söderqvist T. Social media and public health research. Copenhagen, Denmark: University of Copenhagen.
2012 Aug 20. URL: http://www.museion.ku.dk/wp-content/uploads/FINAL-Social-Media-and-Public-Health-Research.
pdf [accessed 2016-11-02] [WebCite Cache ID 6limaWFsM]
Oh S. The characteristics and motivations of health answerers for sharing information, knowledge, and experiences inonline environments. J. Am. Soc. Inf. Sci 2011 Nov 01;63(3):543-557. [doi: 10.1002/asi.21676]
Giustini D. How Web 2.0 is changing medicine. BMJ 2006 Dec 23;333(7582):1283-1284 [FREE Full text] [doi:10.1136/bmj.39062.555405.80] [Medline: 17185707]
U.S. National Library of Medicine. Fact Sheet of the UMLS Metathesaurus. URL: https://www.nlm.nih.gov/pubs/factsheets/
umlsmeta.html [accessed 2016-11-02] [WebCite Cache ID 6limjdjWl]
U.S. National Library of Medicine. SNOMED Clinical Terms. URL: https://www.nlm.nih.gov/healthit/snomedct/index.
html [accessed 2016-11-02] [WebCite Cache ID 6limqaGNJ]
Finnegan R. ICD-9-CM coding for physician billing. J Am Med Rec Assoc 1989 Feb;60(2):22-23. [Medline: 10303229]
Bennett CC. Utilizing RxNorm to support practical computing applications: capturing medication history in live electronic
health records. J Biomed Inform 2012 Aug;45(4):634-641 [FREE Full text] [doi: 10.1016/j.jbi.2012.02.011] [Medline:
22426081]
U.S. National Library of Medicine. Fact Sheet of the UMLS Semantic Network. URL: https://www.nlm.nih.gov/pubs/
factsheets/umlssemn.html [accessed 2016-07-13] [WebCite Cache ID 6iyTFIvxE]
Zeng-Treitler Q, Goryachev S, Kim H, Keselman A, Rosendale D. Making texts in electronic health records comprehensible
to consumers: a prototype translator. AMIA Annu Symp Proc 2007:846-850 [FREE Full text] [Medline: 18693956]
Zielstorff R. Controlled vocabularies for consumer health. Journal of Biomedical Informatics 2003 Aug;36(4-5):326-333.
[doi: 10.1016/j.jbi.2003.09.015]

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

Park et al

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 12(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
47.
48.
49.

50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.

62.
63.
64.
65.

66.

67.

Park et al

Miller EA, Pole A. Diagnosis blog: checking up on health blogs in the blogosphere. Am J Public Health 2010
Aug;100(8):1514-1519. [doi: 10.2105/AJPH.2009.175125] [Medline: 20558802]
Lagu T, Kaufman EJ, Asch DA, Armstrong K. Content of weblogs written by health professionals. J Gen Intern Med 2008
Oct;23(10):1642-1646 [FREE Full text] [doi: 10.1007/s11606-008-0726-6] [Medline: 18649110]
Boulos Maged N Kamel, Maramba I, Wheeler S. Wikis, blogs and podcasts: a new generation of Web-based tools forvirtual collaborative clinical practice and education. BMC Med Educ 2006;6:41 [FREE Full text] [doi:10.1186/1472-6920-6-41] [Medline: 16911779]
Oomen-Early J, Burke S. Entering the Blogosphere: Blogs as Teaching and Learning Tools in Health Education. International
Electronic Journal of Health Education 2007;10:186-196 [FREE Full text]
Cobus L. Using blogs and wikis in a graduate public health course. Med Ref Serv Q 2009;28(1):22-32. [doi:10.1080/02763860802615922] [Medline: 19197741]
Shah C, Oh S, Oh J. Research agenda for social Q&A. Library & Information Science Research 2009;31(4):205-209. [doi:10.1016/j.lisr.2009.07.006]
Statista. Statistics and facts about Tumblr. URL: http://www.statista.com/topics/2463/tumblr/ [accessed 2016-02-06][WebCite Cache ID 6iyTGqcPl]
DearBlogger: The Blogging Answers Community. The Best Places to Start a Blog. URL: http://www.dearblogger.org/
blogger-or-wordpress-better [accessed 2016-11-02] [WebCite Cache ID 6linVHtiQ]
Fitzpatrick J. Lifehacker. 2010 Jun 20. Five Best Blogging Platforms. Lifehacker. 2010 Jun 20. URL: http://lifehacker.com/
5568092/five-best-blogging-platforms [accessed 2016-11-02] [WebCite Cache ID 6linqjSUQ]
Quantcast. Statistics of Yahoo! Answers. URL: https://www.quantcast.com/answers.yahoo.com [accessed 2016-07-13][WebCite Cache ID 6iyTJNzkf]
Temmerman R. Towards new ways of terminology description: The sociocognitive-approach. In: Towards new ways ofterminology description: the sociocognitive-approach. Amsterdam: J. Benjamins; 2000.
Crystal D. Dictionary of Linguistics and Phonetics (The Language Library). Hoboken, NJ: Wiley-Blackwell; Jun 2008.
U.S. National Library of Medicine. UMLS Glossary. URL: https://www.nlm.nih.gov/research/umls/new_users/glossary.
html [accessed 2016-01-08] [WebCite Cache ID 6iyTK76nW]
Apache Software Foundation. cTAKES (clinical Text Analysis and Knowledge Extraction System). 2016 Jan 18. URL:
http://ctakes.apache.org [accessed 2016-01-18] [WebCite Cache ID 6iyTLd7aW]
Garla V, Lo RV, Dorey-Stein Z, Kidwai F, Scotch M, Womack J, et al. The Yale cTAKES extensions for documentclassification: architecture and application. J Am Med Inform Assoc 2011;18(5):614-620 [FREE Full text] [doi:10.1136/amiajnl-2011-000093] [Medline: 21622934]
Baldridge J. Apache Software Foundation. The openNLP project. URL: http://opennlp.apache.org/index [accessed 2016-11-02][WebCite Cache ID 6lioR15Gv]
Apache Software Foundation. The OpenNLP Documentation. URL: https://opennlp.apache.org/documentation.html [accessed
2016-11-02] [WebCite Cache ID 6lioZxO8m]
Garla VN, Brandt C. Semantic similarity in the biomedical domain: an evaluation across knowledge sources. BMC
Bioinformatics 2012 Oct 10;13:261 [FREE Full text] [doi: 10.1186/1471-2105-13-261] [Medline: 23046094]
He Z, Park M, Chen Z. UMLS-Based Analysis of Medical Terminology Coverage for Tags in Diabetes-Related Blogs. In:
Philadelphia, PA. 2016 Presented at: iConference 2016; March 20-23, 2016 URL: https://www.ideals.illinois.edu/handle/
2142/89441
Sarasohn-Kahn J. The Wisdom of Patients: Health Care Meets Online Social Media. California Health Care Foundation.
2008 Apr. URL: http://www.chcf.org/publications/2008/04/the-wisdom-of-patients-health-care-meets-online-social-media[accessed 2016-11-02] [WebCite Cache ID 6liosAzP6]
Zhang Y. Beyond quality and accessibility: Source selection in consumer health information searching. J Assn Inf Sci Tec
2014 Jan 07;65(5):911-927. [doi: 10.1002/asi.23023]

Abbreviations
APIs: Application Program Languages
AUI: atom unique identifier
CSP: Computer Retrieval of Information on Scientific Projects Thesaurus
CUI: concept unique identifier
IC: information content
LCH NW: Library of Congress Subject Headings, Northwestern University subset
LOINC: Logical Observation Identifier Names and Codes
MeSH: Medical Subject Headings
NCIt: National Cancer Institute Thesaurus
NDFRT: National Drug File – Reference Terminology
NER: named entity recognition
https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 13(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Park et al

NLM: National Library of Medicine
NLP: natural language processing
OAC CHV: Open-Access Collaborative Consumer Health Vocabulary
POS: Part-Of-Speech
Q&A: questions and answers
SNOMED CT: SNOMED Clinical Terms
STY: semantic type
UIMA: Unstructured Information Management Architecture
UMLS: Unified Medical Language System
WSD: word sense disambiguation

Edited by G Eysenbach; submitted 10.03.16; peer-reviewed by C Kruse, H Wu, S Kim; comments to author 28.06.16; revised version
received 02.08.16; accepted 22.10.16; published 24.11.16
Please cite as:
Park MS, He Z, Chen Z, Oh S, Bian J
Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites
JMIR Med Inform 2016;4(4):e41
URL: https://medinform.jmir.org/2016/4/e41/
doi: 10.2196/medinform.5748
PMID: 27884812©Min Sook Park, Zhe He, Zhiwei Chen, Sanghee Oh, Jiang Bian. Originally published in JMIR Medical Informatics(http://medinform.jmir.org), 24.11.2016. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete
bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license
information must be included.

https://medinform.jmir.org/2016/4/e41/

XSL• FO
RenderX

JMIR Med Inform 2016 | vol. 4 | iss. 4 | e41 | p. 14(page number not for citation purposes)