JMIR MEDICAL INFORMATICS

Tapi Nzali et al

Original Paper

What Patients Can Tell Us: Topic Analysis for Social Media on
Breast Cancer
Mike Donald Tapi Nzali1,2, PhD; Sandra Bringay2,3, PhD; Christian Lavergne1,3, PhD; Caroline Mollevi4, PhD; Thomas
Opitz5, PhD
1

Institut Montpelliérain Alexander Grothendieck (IMAG), Department of Mathematics, Montpellier University, Montpellier, France

2

Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), Department of Computer Science, Montpellier University,
Montpellier, France
3

Paul Valery University, Montpellier, France

4

Biometrics Unit, Institut du Cancer Montpellier (ICM), Montpellier, France

5

BioSP Unit, Institut National de la Recherche Agronomique (INRA), Avignon, France

Corresponding Author:
Mike Donald Tapi Nzali, PhD
Institut Montpelliérain Alexander Grothendieck (IMAG)
Department of Mathematics
Montpellier University
Place Eugène Bataillon
Case Courrier 051
Montpellier, 34095
FrancePhone: 33 4 67 41 86 72
Fax: 33 4 67 41 85 00
Email: tapinzali@lirmm.fr

Abstract
Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual
resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of
retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast
cancer.
Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional andsymptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical
trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30]
and breast cancer module [QLQ-BR23]).
Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics
discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted
from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant
preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the
topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life.
Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patientson social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group
topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media
topics had no corresponding topic in the questionnaires, which do not cover all of the patients’ concerns. Of these 5 topics, 2
could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in thecancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus.
Conclusions: We found a good correspondence between detected topics on social media and topics covered by the
self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging
topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed thatsocial media mining is an important source of information for complementary analysis of quality of life.
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Tapi Nzali et al(JMIR Med Inform 2017;5(3):e23) doi: 10.2196/medinform.7779
KEYWORDS
breast cancer; text mining; social media; unsupervised learning

Introduction
Social media such as Facebook, Twitter, or Internet forums
dedicated to health-related topics have evolved into easily
accessible participatory tools for the exchange of knowledge,
experience, and opinions through structured collections of text
documents [1]. Online health forums are used by patients to
exchange information [2]. Patients maintain their anonymity
while discussing freely with other patients. Whereas
communication with doctors and the medical staff in hospitals
mainly revolve around technical issues of the disease andtreatment, social media give patients access to more generalexchanges of information, experiences, and mutual support
among former and current patients [3]. Such forums cantherefore be considered as a valuable resource for the study ofhealth-related quality of life (QoL). As shown by some studies(eg, [4]), the anonymous environment of social media facilitates
the unbiased expression of opinions and of feelings such as
doubt or fear. Internet users have been shown to be primarily
interested in specific information on health problems or diseases[5-7] and in adopting a healthier lifestyle and looking foralternative points of view [5]. Here we propose an approach tostructure and evaluate clinically relevant information in
narratives extracted from online health social media, with afocus on the QoL of patients with breast cancer.
While constant progress in medical science leads to newtreatments and improved chances to prolong lives, suchtreatments can be difficult to undergo. QoL can be considered
as an alternative clinical end point in this context, moving the
focus away from quantity to quality [8-11]. QoL falls within
the scope of patient-reported outcomes; that is, measures ofperceived health [12,13]. These measures must therefore be
reported by patients themselves. For instance, alternative
treatments such as palliative treatment of terminal cancer may
be less efficient from a traditional clinical stance but may still
be preferable with respect to the patients’ QoL [14,15].
Moreover, health economists must take into account the expenseof treatments with respect to their effective benefits, for instancemeasured by the improvement in QoL (see Hirth et al [16] andCutler and McClellan [17] for a general discussion, and Hillner
and Smith [18] for a cost-effectiveness study of chemotherapy
in certain cases of breast cancer).
Since QoL is a multidimensional, subjective, andculture-dependent concept, its quantification is not as
straightforward, as shown in the literature review of Garratt etal [19]. This concept includes at least physical, psychological,
and social well-being, as well as symptoms related to illness
and treatment. Today, QoL is assessed in cancer clinical trials
by self-administered questionnaires developed by the European
Organization for Research and Treatment of Cancer (EORTC).
The EORTC Quality of Life Questionnaire Core 30 (QLQ-C30)[20] is a generic self-administered questionnaire often associatedwith disease-specific modules, such as the EORTC breast cancer
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module (QLQ-BR23). The EORTC QLQ-C30 contains 30 items
and evaluates 15 dimensions of QoL: 5 functional scales, 1 QoL
and global health status scale, and 8 symptomatic scales, as well
as 1 scale measuring the financial difficulties associated withthe disease. The EORTC QLQ-BR23 contains 23 questions. It
is usually administered with the EORTC QLQ-C30 and isdesigned to measure QoL for breast cancer patients at various
stages and with different treatment modalities. The evaluationconsists of 4 functional scales and 4 symptomatic scales.
Usually, self-administered questionnaires evaluate functionaland symptomatic dimensions and are filled in at a predefinedtime of the study protocol, such as at baseline, during treatment,
and at follow-up. In this context, an advantage of social media
is that they allow patients to leave a written trace of their
sentiment at any time, therefore avoiding potential self-reporting
bias owing to a change of perception due to time lag.
Opitz et al [21] developed an automated approach for thesupervised detection of topics defined in QLQ-BR23
questionnaire items for cancerdusein.org, a French forum
specialized in breast cancer. In this new work, we used an
unsupervised method to discover topics covered by health social
media. Unsupervised methods have been successfully applied
to biomedical data. For example, Arnold and Speier [22]presented a topic model tailored to the clinical reporting
environment that allows for individual patient timelines. Lu etal [23] used text clustering algorithms on social media data todiscover health-related topics. Zhang et al [24] applied a
convolutional neural network classifier to an online breast cancer
community and carried out a longitudinal analysis to show topic
distributions and topic changes throughout the members’
participation. In our study, the main medical application was
to help improve questionnaires by including new topics ofinterest for patients (topics frequently discussed by patients andthe impact on QoL) as new items in the questionnaires.
Researchers have developed several topic models, including
latent semantic analysis [25], probabilistic latent semantic
analysis [26], latent Dirichlet allocation (LDA) [27], and latentsemantic indexing [28]. In this study, we defined a generalprocess based on LDA [27] and applied this model to social
media. LDA, an unsupervised generative probabilistic method
for modeling a corpus, is the most commonly used topic
modeling method. The main disadvantage of LDA is that there
are no objective metrics that justify the choice of thehyperparameters. However, the main advantage of LDA is that
it is a probabilistic model with interpretable topics. Nowadays,a growing number of probabilistic models are based on LDA
and dedicated to particular tasks. For example, Zhan et al [29]used LDA to identify topics among posts generated by
e-cigarette users in social media. Wang et al [30] and Paul andDredze [31] constructed a specialized and advanced LDA modelusing biomedical terms to provide a more effective way of
exploring the biomedical literature. LDA has also beensuccessfully used for patient-generated data [32-36] and in
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Tapi Nzali et al

particular for online breast cancer discussions [3,24]. Hao andZhang [37] used LDA to examine what Chinese patients said
about their physicians in 4 major specialty areas. Hao et al [38]used LDA to identify topics in positive and negative textual
reviews of obstetricians and gynecologists from the 2 mostpopular online doctor rating websites in the United States andChina. Yesha and Gangopadhyay [39] described methods toidentify topics and patterns within patient-generated data related
to suicide and depression. LDA has also been used as a feature
to build machine learning models to automatically identify theextent to which messages contain emotional and informational
support on online health forums dealing with breast cancer [40]or on Chinese social media [41].
Conducting automated research as we have done here is of
considerable interest for processing a large amount of text
obtained from social media. The LDA approach for extracting
topics allows for better targeting for information exploration,
reducing search time, and treating topics as a flat set ofprobability distribution; it can also be used to recover a set of
topics from a corpus. In this work, we only used the LDA modeland tuned parameters to align the topics found with QoL
questionnaires. The originality of our approach is toautomatically relate the topics obtained with the LDA method

to the questionnaire items with an adaptation of the Jaccard
coefficient.
In this study, the purpose of our approach was diverse: (1) toprovide a nonconventional analysis of QoL from social media
and put the topics identified with this nonconventional analysis
into perspective with those of classical QoL questionnaires
collected in clinical trials (in particular in breast cancer: EORTCQLQ-C30 and QLQ-BR23); (2) to apply the LDA model topatient data with relevant pretreatments; (3) to index thenarratives with respect to topics extracted through anunsupervised statistical analysis of forum content and topredefined topics from questionnaires used in cancer clinical
trials; and (4) to discover new topics directly from patients’concerns that are not included in the current questionnaires used
to evaluated QoL, with the possibility that these topics could
be included in these questionnaires if sufficiently relevant.

Methods
Data
Data Description
In this work, we used datasets from 2 different social media
sources: cancerdusein.org and Facebook groups. Table 1
summarizes statistics from these 2 datasets.

Table 1. Number of users, threads, and posts on a social network and a health forum analyzed in this study.
Characteristics

Health forum (cancerdusein.org)

Social network (Facebook groups)

Date

October 2010-October 2014

October 2010-October 2014

No. of users

675

1394

No. of discussion threads

1050

11,013

No. of messages

16,86870,092

The first dataset contained the forum posts fromcancerdusein.org, a French health forum with more than 16,000
posts. These posts cover a large number of topics related tohealth issues. This forum is recommended to patients in abrochure of the Institut National du Cancer (INCA), which is
the French reference organization in oncology. The forum is
recommended for patients to exchange information and find
comfort and potential solutions to their problems. It serves as
an online cancer support community, where cancer patients,
cancer survivors, and their families share information about
cancer and their conditions. The second dataset contains posts
from groups on Facebook, one of the most well-known social
networks. We extracted 70,092 posts from 4 different public
groups or communities on Facebook: Cancer du sein, Octobrerose 2014, Cancer du sein - breast cancer, and brustkrebs. We
collected data from groups focusing on the adult population(the targeted users) and in which users were very active.
On both social media platforms, patients freely exchange
information without the need for moderators to supervise
discussions. New messages can either be added to an existing
thread or be posted to open a new thread. In cancerdusein.org,
a thread appears in exactly 1 of the 13 predefined subforums,
for example, Discussion générale [general discussion], Vivre
mon cancer au quotidien [daily life with my cancer], Les bonnes
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nouvelles [good news], or Récidives et combats au long cours[relapses and long-term battles]. In Facebook groups, there are
no predefined topics to index the threads. Structuring topics
according to the subforum structure is possible incancerdusein.org, but this structure underlines the relatively
uninformative and widely spread topics, covering a strongly
unbalanced number of messages. Such indexing is not possible
in Facebook groups. Interestingly, we propose to accomplish afiner analysis of topics in the next section, which further enables
the presence of several topics within 1 message.

Data Preprocessing
Texts on social media are often strongly heterogeneous andnoisy, with many deviations from standards of spelling, syntax,
and abbreviations, which impede efficient natural language
processing. The French language has a rich spelling andgrammar, characterized by special characters such as ç, various
kinds of accented vowels (eg, é, è, ê, ë, â, and à), and many
flexional variants. Additional rules exist for linking subsequentterms in certain situations (eg, the contraction du formed fromde+le and the contraction des formed from de+les). As aconsequence, automatic correction of text not obeying thoserules is relatively difficult in practice. Furthermore, semantic
analysis of texts is complicated by a large number of homonymy
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relationships: for example, pas can either mean step (noun) or
can be the negation adverb not. As Balahur [42] and Farzindarand Inkpen [43] have pointed out, these linguistic peculiarities
may affect classification performance. For this reason, wedeveloped the following preprocessing steps.
•

•

•
•
•
•
•

•

•

Removal of user tags. All user tags that have been identified
in our corpus are removed, for example, @name,@surname.
Replacement of hyperlinks and email addresses. All thehypertext links are replaced by the term “link” and all theemail addresses are replaced by the term “mail.” Hyperlinks(Internet, email, etc) are deleted. Emoticons are coded as:smile:, :sad:, etc.
Replacement of slang. Some expressions frequently usedon social media, such as lol, mdr[lol], and xD, are removed.
Lemmatization. All words are lemmatized (using
TreeTagger [44]).
Lowercasing. Capitals letters are lowercased.
Removal of stopwords.
Replacement of specific patient terms. The texts for the 2
corpora are usually highly focused on a specific domain(breast cancer, in our case). Most often, as patients arelaypersons in the medical field, they use slang,abbreviations, and their own vocabulary during theirexchanges. To automatically analyze text from social
networks, we need a specific vocabulary. In this work, we
use the vocabulary created by Tapi Nzali et al [45] toreplace the patients’ terms with biomedical terms used byhealth professionals and presented in shared medical
resources. For example, crabe [crab] is replaced by cancer,
onco is replaced by oncologue [oncologist].
Correction of spelling. Spelling correction is important toremove redundant dimensions of data and to improvepart-of-speech tagging, which is the basis for manystatistical and rule-based methods in natural language
processing. We apply spelling correction based on
specialized dictionaries constructed ad hoc and the open
source tool GNU Aspell version 0.60.6.1, whose algorithm
proposes a list of possible corrections for unknown terms
from the corpus. We use the following ad hoc dictionaries:
lists of breast cancer drugs and of secondary effects, andproper names extracted from forum metadata (usernames,
user residence) and from narratives (terms with capital first
letter not at the beginning of a sentence; usernames
identified from salutations at the beginning of forum posts).
Extraction and deletion of forum pseudonyms. All thepseudonyms, previously extracted from each website, areused. The pseudonyms are extracted and deleted if they
exist in the post.

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Tapi Nzali et al

Unsupervised Topic Detection and Assigning
Modeling Topics With Latent Dirichlet Allocation
Today, detection of latent semantic structures and topics has
become a very active field of research in the text mining
community. We focused on the LDA model [27], which has
become a standard model for unsupervised topic detection froma text corpus. It is a probabilistic model with a hierarchicaldefinition of its components. With the LDA model, we generated
new documents from a given model. Based on the relatively
simple and robust bag-of-words representation of text
documents, it leaves the order of occurrence of terms andsentence structure out for consideration. For a given corpus of
D documents, we first defined the relevant vocabulary V, apreprocessed collection of terms occurring in the corpus. Typicalpreprocessing steps include spelling correction, lemmatization,and the removal of noisy or irrelevant terms. To define a topict, we associated a nonnegative weight ωti with each of the
vocabulary’s terms, wi, so that weights summed up to 1 (∑Vi=1
ωti=1). In practice, each topic typically consisted of a relatively
small number of terms with nonnegligible weight. An LDA
model uses a fixed number K>1 of topics. For each documentd, weights ωdt≥0 indicate the occurrence probability of terms
from topic t, where the sum of ωdt over all topics t yields 1(∑Kt=1 ωdt=1). If document d contains ld terms (or “positions”),
we associated a topic tdj with each of the positions j=1,..., ld,
where the probability of associating topic t is αdt. Finally, eachposition was filled with a term, wdj, from the vocabulary, where
the probability of using term wi is ωtdj.
The corpus-generation model is proposed by the algorithm
shown in Figure 1.
The principal information that we can learn from using such amodel on a corpus of text data is the structure of represented
topics and the distribution of topics over the documents
contained in the corpus. The high number of unknown
parameters in this model makes inference challenging, yet
Bayesian techniques such as Gibbs sampling [46] have provenreliable. Based on prior assumptions about the distribution of
the weights of terms in topics and of topics in documents on a
range from very uniform to very spiked, these inferencetechniques are applied to the data to estimate the posterior
distributions of the model. Most importantly, the most likely
topic structure and the occurrence probabilities for topics ineach document are proposed. In this work, we considered amessage as a document.

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Figure 1. Algorithm proposing the corpus-generation model.

Crucial Model Parameters
Besides K, 2 parameters often denoted as α and β strongly
influence the distribution of topic probabilities for each of themessages. They are concentration parameters for the priordistributions of topics over a message (α) and of words over a
topic (β). When α or β is smaller than 1 and decreases, priormass concentrates closer and closer to the border of the simplex
with spikes at each of its vertices. Then, 1 or fewer components(topics for α, words for β) carry strong probability in the mixture
distribution. In the limit 0, a single component is selected witha probability of 1. On the contrary, when α or β is larger than
1 and increases, mass concentrates more and more in thebarycenter of the simplex, leading to a mixture of the
distribution, which is more and more balanced over allcomponents. In the limit ∞, each component is selected with aprobability of 1 over the number of components.
Now we will explain our choice of α based on the influence of
α on the distribution of topic probabilities for messages and ofterm distributions for topics. When α=1, the prior distribution
for the vector of topic probabilities corresponds to a uniform
distribution on the simplex with K vertices. As α increases, thedistribution concentrates more and more strongly toward thecenter of the simplex, such that most of the probabilities arecloser to 1/ K. As α decreases, it concentrates more and morestrongly toward the vertices, leading to some probabilities being
further away from 1/ K. For fixed α, probabilities concentrate
more and more around 1/ K as K increases. In Griffiths andSteyvers [47], values α=α0/ K with the constant α0=50 are
encouraged, where dividing through K constantly keeps a certain
complexity measure of the model. Exploratory analysis showed
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that α0=50 led to very flat probability vectors in our case, which
made it difficult to attribute a small number of topics forindexation to each message. On the other hand, smaller valuesof α0 led to topics becoming more difficult to interpret due toflatter distribution of term probabilities within topics and similar
dominating terms in multiple topics. After careful analysis of
topics and posterior distributions for a range of values of α0,
we decided to fix α0=10. Whereas higher values of α0 yieldeda better fit of the model in terms of its likelihood, it led to very
flat posterior probabilities for the topic distribution of messages.
As in Griffiths and Steyvers [47], we decided to fix the valueof parameter β to 0.1 for our experiments.
There is evidence [48] that automatic choice of parameters
through a model selection criterion may result in an
unsatisfactory topic collection, whose interpretation is morechallenging than topics associated with suboptimal values of
the criterion. Often, the calculation of held-out likelihood isused, allowing for approaches such as likelihoodcross-validation. However, the likelihood calculation is nottrivial, and some standard methods produce inaccurate results(see [49]).

Vocabulary Definition
To avoid noisy topics that are difficult to interpret, it is useful
to focus on terms with potential medical relevance. Here, we
defined terms as sequences of words, and often there was onlya single word. To begin, we used terms indexed in the French
version of the Medical Subject Headings (MeSH) [50]. Then
we added terms figuring in a list of breast cancer drugs(extracted from the online resource) or appearing in a list of
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nonconventional treatments (extracted from the French
Wikipedia entry). We denoted this term set as MED. We retained
481,111 occurrences of 18,672 terms in 16,868 messages oncancerdusein.org, and 626,043 occurrences of 18,741 terms in
70,092 messages on Facebook. The resulting topics, often
strongly dominated by a single term, appeared to be rather
difficult to interpret by clinical experts, possibly due to therelatively small dimension of the term-document space. We
categorized terms figuring in the representative terms according
to their grammatical role: nouns/proper names (NN), verbs (V),and adjectives (A). Then, we extracted topics by applying LDA
to the original MED term set, extended by terms according toscenarios MED+NN+V+A. Based on the exploratory inspectionof topics extracted by LDA in the approaches presented in thefollowing, we further removed a small number of strongly

Tapi Nzali et al
represented terms leading to strong noise (femme [woman],temps [time or weather]), and medically meaningless topics.

Align Topics and Questionnaires
With the topics returned by the LDA model, we automatically
identified correspondences between the topics and thequestionnaires, as shown in Figure 2. To align topics andquestionnaires, we computed a distance between each question
qj and all topics ti in T. We kept the topic with the higher
distance. To compute the distance between an LDA topic andan item of the questionnaire, we customized the Jaccard
coefficient [51] by taking into account the probability of thewords obtained with the LDA model, as shown in Figure 3(equation 1).

Figure 2. Automatic identification of correspondences between topics and questionnaires. LDA: latent Dirichlet allocation; MED + NN + V + A: setof medically relevant terms (MED) extended by terms categorized by their grammatical role (NN: nouns and proper names; V: verbs; A: adjectives).

Figure 3. Equation to calculate the distance between a latent Dirichlet allocation topic and an item of the questionnaire.

Results
Topic Modeling Result
To run experiments, we used the R package LDA [52] and the
R environment version 3.2.5 (R Foundation) for the
implementation. We tested different scenarios, and an expert
validated and labeled the topics and verified the association
between topics and questionnaires items. The expert is abiostatistician and QoL researcher in the cancer field [53,54].
In scenario MED + NN, most of the topics were of a factual
nature, whereas scenario MED + NN + V led to a more complete
description of topics, where verbs often add information about
actions undertaken by users and other stakeholders (wait,
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consult, seek, support, etc) and about user sentiment (feel, cry,tire, fear, accept, etc). In scenario MED + NN + V + A, several
topics consisting mainly of emotional words were difficult tointerpret from a medical point of view. We reported the stabilityof the majority of topics that were identified through thescenarios MED + NN, MED + NN + V, and MED + NN + V +A due to the similarity of dominating terms. After carefulanalysis, we narrowed down the choice of K to a value between
20 and 30. With more than 20 topics, we found duplication of
topics (2 topics may deal with the same subject). In addition,some are unable to be interpreted (the medical expert found no
meaning). Consequently, we decided to retain scenario MED +NN + V + A with 20 topics. Finally, we fixed K=20 for theduration of this study. For each topic, we showed only 20
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keywords having higher probabilities under that topic. These
keywords were presented to the expert. Table 2 and Table 3 list
the topic modeling results of the 2 corpora. We show the top
10 keywords for each topic. Table 4 shows the results of the 20
topics interpreted by the medical expert on the 2 corpora.

To find the theme corresponding to a question, we used equation
1 (Figure 3) proposed above. We obtained the following
relationships:•

Relationships Between Questionnaire Topics
In this work, we used 2 QoL questionnaires (EORTC QLQ-C30
and EORTC QLQ-BR23) to look for relationships between thestudied dimensions in these previous questionnaires and topics
that we interpreted. The EORTC QLQ-C30 is a 30-item,
self-administered, cancer-specific questionnaire designed tomeasure QoL in the cancer population. The assessment
comprises 5 functional scales (physical, role, cognitive,
emotional, and social), 8 symptomatic scales (fatigue, nausea
and vomiting, pain, dyspnea, insomnia, loss of appetite,
constipation, and diarrhea), and 1 scale measuring financial
difficulties and 1 measuring global health status and QoL by ascore ranging from 0 to 100 through the 30 items [20]. The
EORTC QLQ-BR23 is a 23-item, self-administered, breast
cancer-specific questionnaire, usually administered with the
EORTC QLQ-C30, designed to measure QoL in the breast
cancer population at various stages and with patients withdiffering treatment modalities. The assessment comprises 4
functional scales (body image, sexual functioning, sexualenjoyment, and future perspective) and 4 symptomatic scales(systemic therapy side effects, breast symptoms, arm symptoms,
and hair loss) [55]. The EORTC health-related QoLquestionnaires are built on a Likert scale with polytomous items.

••

Topic sexuality is related to items 44 (To what extent wereyou interested in sex?) and 45 (To what extent were you
sexually active?).
Topic hair loss is related to item 34 (Have you lost any
hair?).
Topic body care and body image during cancer is related
to items 39 (Have you felt physically less attractive as aresult of your disease or treatment?) and 40 (Have you beenfeeling less feminine as a result of your disease ortreatment?).

These relationships were validated by a medical expert.
Following validation of the results, we calculated the precision.
On cancerdusein.org data, for the 53 items, 39 relationships
with topics were validated by the medical expert and 14 wereinvalidated, for a precision of 74%. On Facebook data, for the
53 items, 36 relationships were validated by the medical expert
and 17 were invalidated, for a precision of 68%. The medical
expert also manually examined the invalidated relationships.
This step reduced the time spent by the expert to findrelationships between the questions and the topics. The obtained
precision rates can be explained by the fact that the items of thequestionnaires are composed of very short sentences. On
average, these sentences contain fewer than 5 words.

Table 2. Top 10 frequently occurring words for the first 10 topics (among the 20 found) on cancerdusein.org forum data.

aTopic labela

Topic
no.

Top 10 words with their translation

1

cheveu, perdre, perruque, tomber, tête, commencer, repousser, hair, lose, wig, fall, head, begin, regrowth,chimiothérapie, perte, foulard
chemotherapy, loss, scarf

2

prendre, temps, travail, demander, soin, reprendre, charge,
travailler, aide, payer

3

effet, chimiothérapie, secondaire, cure, douleur, passer, mam- effect, chemotherapy, secondary, treatment, pain,
mographie, nausée, docétaxel, fatigue
pass, mammography, nausea, docetaxel, fatigue

Chemotherapy and its
secondary effects

4

prendre, effet, douleur, traitement, problème, tamoxifène, prise, take, effect, pain, treatment, problem, tamoxifen,
penser, secondaire, arrêter
catch, think, secondary, stop

Hormone therapy andits secondary effects

5sein, bras, chirurgie, reconstruction, opération, douleur, prothèse, opérer, enlever, cicatrice6

baiser, petit, beau, super, attendre, soutien, nouveau, guerrier, kiss, little, beautiful, great, wait, support, new,grand, vérité
warrior, big, truth

7

ongle, peau, radiothérapie, main, séance, pied, rayon, brûlure, nail, skin, radiotherapy, hand, session, foot, radius, Radiotherapy and its
crème, conseil
burn, cream, council
secondary effects

8

prendre, manger, boire, essayer, miel, aider, produit, demander, take, eat, drink, try, honey, help, product, ask,
santé, complément
health, complement

Complementary and alternative medicine

9lire, forum, message, venir, nouveau, donner, trouver, site,
réponse, écrire

read, forum, message, come, new, give, find, site,
response, write

Media and forum information exchange

10homonymie, enfant, fille, maman, vie, cancer, vérité, vivre,
malade, famille

homonymy, child, girl, mom, life, cancer, truth,
live, sick, family

Family background andbreast cancer

FrenchEnglish translation
Hair loss

take, time, job, ask, care, restart, charge, work, help, Work life during cancerpay
and financial aspects

breast, arm, surgery, reconstruction, operation, pain, Breast reconstruction
prosthesis, operate, remove, scar
Support from patient’s
family and friends

Topic label was assigned by a medical expert.

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Table 3. Top 10 frequently occurring words for the first 10 topics (among the 20 topics found) on Facebook data.

aTopic labela

Topic
no.

Top 10 words

1

voir, attendre, résultat, médecin, oncologie, examen, biopsie, see, wait, result, doctor, oncology, examination,
mammographie, contrôle, scanner
biopsy, mammography, test, scanner

Diagnosis

2

douleur, effet, chimiothérapie, secondaire, jour, prendre, mal, pain, effect, chemotherapy, secondary, day, take,fatigue, nausée, chaleurbad, fatigue, nausea, heat

Chemotherapy and its
secondary effects

3

justice, moral, garder, aller, fort, dureté, battre, étape, force, justice, morale, keep, go, strong, hardness, beat,combatstep, strength, fightBreast cancer as a daily
battle4cheveu, perdre, tomber, repousser, perruque, couper, raser,tête, joli, foulard

hair, lose, fall, growth, wig, cut, shave, head,
beautiful, scarf

Hair loss

5

prendre, suivre, dire, soin, arrêter, traitement, tamoxifène,
poids, perdre, homonymie

take, follow, tell, care, stop, treatment, tamoxifen,
weight, lose, homonymy

Secondary effect oftreatment

6aller, justice, passer, sexologie, allergologie, baiser, penser,
meilleur, voir, reposergo, justice, pass, sexology, allergology, kiss, think, Body care and body
best, see, rest
image during cancer

7

homonymie, dire, vérité, suivre, peur, sexologie, comprendre, homonymy, tell, truth, follow, fear, sexology, uncroire, dureté, enfant
derstand, believe, hardness, child

Family background andbreast cancer

8

demander, suivre, droit, travail, aide, médecin, payer, charge, ask, follow, law, job, help, doctor, pay, charge,
travailler, donner
work, give

Work life during cancer
and financial aspects

9sein, opération, reconstruction, enlever, bras, opérer, mastec- breast, operation, reconstruction, remove, arm, op- Breast reconstruction
tomie, cicatrice, retirer, prothèseerate, mastectomy, scar, withdraw, prosthesis

10

suivre, aller, fille, sol, voir, rire, regarder, marier, croire, lire follow, go, girl, ground, see, laugh, look, marry,
believe, read

FrenchEnglish translation

Support from patient’s
family and friends

Topic label was assigned by a medical expert.

Table 4. List of identified topic titles with K=20 in collaboration with an expert.
Topic
no.

cancerdusein.org

Facebook

1

Hair loss

Diagnosis

2

Work life during cancer and financial aspects

Chemotherapy and its secondary effects

3

Chemotherapy and its secondary effects

Breast cancer as a daily battle

4

Hormone therapy and its secondary effects

Hair loss

5

Breast reconstruction

Secondary effects of treatments

6

Support from patient’s family and friends

Body care and body image during cancer

7

Radiotherapy and its secondary effects

Family background and breast cancer

8Complementary and alternative medicine

Work life during cancer and financial aspects

9

Media and forum information exchange

Breast reconstruction

10

Family members with breast cancer

Support from patient’s family and friends

11

Treatment period

Interaction with nurses and doctors

12

Everyday life during cancer

Anxiety and fatigue

13

Healing

Healing of family member

14

Search for medical information

Relapse15

Mourning

Sexuality

16

Diagnosis

Body care and body image during cancer

17

Breast cancer as a daily battle

Family members with breast cancer

18

Body care and body image during cancer and sexuality

Healing

19

Surgery

Support from patient’s family and friends

20

Waiting for results of analysis, concerns

Treatment period

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Table 5 shows the relationships between topics fromquestionnaires and those we found in the 2 corpora. The firstcolumn lists the topics of the 2 questionnaires, with thecorresponding questionnaires items shown in column 2.
Columns 3 and 4 give the corresponding topics obtained with
LDA in the 2 corpora. Table 6 shows the percentage of
documents belonging to each topic in cancerdusein.org andFacebook. We noticed that the numbers of messages belonging
to each topic are almost equal; this shows the importance of all
the topics that we found and that were discussed by patients.

Data From cancerdusein.org
We succeeded in interpreting the 20 topics obtained from theoutput of our model on the cancerdusein.org corpus. Table 2
presents the 10 first topics and the top 10 words obtained byour model that were interpreted by an expert. Some relationships
were established. In the QLQ-C30, we found matches for all of
the topics except for global health status and QoL. In the
QLQ-BR23 form, we matched all of the topics.

Data From Facebook
We succeeded in interpreting the 20 topics obtained from theoutput of our model on the Facebook corpus. Table 3 presents
the 10 first topics and the top 10 words obtained by our modelthat were interpreted by an expert. Some relationships were
established. In the QLQ-C30, we found matches for all of the
topics except for role functioning, cognitive functioning, andglobal health status and QoL. In the QLQ-BR23 form, wematched all of the topics.

Tapi Nzali et al
However, in a previous work [57], we proposed a method toautomatically deduce the role of the forum user. This method
can be used at the beginning of our chain to exclude the postsof individuals who are not actual patients.

Generalization of the Method
The second limitation is that we harvested data from only 1
forum and different Facebook groups. However, this forum isfrequently recommended by French physicians to patients. It isalso recommended by INCA, which is the French reference
organization in oncology. We deliberately selected this forum
and these Facebook groups to examine similarities anddifferences within and between these 2 particular communities.
Of course, there are certainly many other online communities
related to breast cancer, and the users in these 2 online
communities were not necessarily representative of users of allbreast cancer social media.
It is also important to note that our method can be easily applied
to other diseases. For example, we can (1) use brain cancer
forum data to align topics discussed by patients with items of
the EORTC QLQ-C30 and the brain cancer module(QLQ-BN20) [58] questionnaires, and (2) use lung cancer forum
data to align topics discussed by patients with items of the
QLQ-C30 and the lung cancer module (QLQ-LC13) [59] We
have already also applied a similar approach to study othersocial media data such as Twitter [60]. The main adaptation isrelative to the acquisition of the patient terms, which are specificto the disease and the social media as mentioned in the Data
Preprocessing section above.

Discussion

Latent Dirichlet Allocation Model

We have presented what we believe to be the first study of healthsocial media data in French, as a potential source of analysis of
the QoL for breast cancer patients. We used accurate machinelearning models to identify topics discussed in online breast
cancer support groups. Then we examined the relationships
between the discovered topics and studied dimensions from
QoL self-administered questionnaires. Exploratory and in-depth
analysis of these data is a potential source of candid information
as an alternative to analysis of QoL based on self-administeredquestionnaires.

A third limitation was the choice of LDA. LDA requires muchmanual tuning of its parameters, which vary from task to task.
We spent a lot of time finding the best parameters so that theresults could be interpreted meaningfully. Such analysis makes
itself a sort of “overfitting” to the task at hand, making it very
hard to generalize the method to other datasets and other tasks.
However, we efficiently defined parameters of 2 types of text
(forum and Facebook posts), which can be reused for otherstudies on comparable corpora.

Limitations
Patient-Authored Text
The first limitation of this study is the type of users, which
produced the patient-authored text exploited in our process.
Indeed, unless a group has formal gatekeeping of members, it
is difficult to know for sure whether people posting to a forum
or in a Facebook group are patients, survivors, health care
professionals, care providers, family, or friends of patients.
Consequently, topics extracted with our method may have been
generated by users who do not have breast cancer. In particular,
it has been known for decades that health information is sought
principally by friends or family members, and then after thatby patients [56]. In this work, we assumed that the relatives’
topics of interest were similar to patients’ topics of interest.

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Topics covered on social media focused on a specific domain,breast cancer. It was difficult to adjust the number of topics
because topics were closed: all of the users were discussing
breast cancer. When we adjusted the model and sought the
optimal K with methods such as those used in other studies (eg,[47,61,62]), we obtained more than 50 topics. An interesting
perspective was using the heuristic approach defined by Zhao
et al [63] to determine an appropriate number of topics. Thismethod is based on the rate of perplexity change [62,64]. Thismeasure is commonly used in information theory to evaluate
how well a statistical model describes a dataset, with lower
perplexity denoting a better probabilistic model [63]. Finally,
as in Arnold et al [65], we observed that an expert is not able
to interpret so many topics. In this study, we manually fixed
K=20. We interpreted all the topics with minimal redundancies.

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Tapi Nzali et al

Table 5. Distribution of documents on each topic on cancerdusein.org and Facebook.
Questionnaires and their scales

Questionnaire
items

cancerdusein.org

Facebook

1-5

Everyday life during cancer

Treatment period

EORTC QLQ-C30a
Functional scales
Physical functioning

Treatment period
Role functioning

6, 7

Everyday life during cancer

Emotional functioning

21-24

Diagnosis

Diagnosis

Breast cancer as a daily battle

Breast cancer as a daily battle

Waiting for results of analysis, concerns

Anxiety and fatigue

Support from patient’s family and friends Support from patient’s family andfriends
Cognitive functioning

20, 25

Search for medical information
Media and forum information exchange

Social functioning

26, 27

Support from patient’s family and friends Support from patient’s family andfriends
Work life during cancer and financial aspects

Work life during cancer and financial
aspects

Chemotherapy and its secondary effects

Anxiety and fatigue

Symptom scales
Fatigue10, 12, 18

Secondary effects of treatments
Nausea and vomiting

14, 15Chemotherapy and its secondary effects

Secondary effects of treatments

Pain

9, 19

Chemotherapy and its secondary effects

Secondary effects of treatments

Surgery
Dyspnea8

Chemotherapy and its secondary effects

Secondary effects of treatments

Insomnia

11

Chemotherapy and its secondary effects

Secondary effects of treatments

Appetite loss

13

Chemotherapy and its secondary effects

Secondary effects of treatments

Constipation

16

Chemotherapy and its secondary effects

Secondary effects of treatments

Diarrhea

17

Chemotherapy and its secondary effects

Secondary effects of treatments

Financial difficulties

28

Work life during cancer and financial aspects

Work life during cancer and financial
aspects

Breast reconstruction

Breast reconstruction

Global health status
Global health status and quality of life 29, 30
EORTC QLQ-BR23b
Functional scales
Body image

39-42

Body care and body image during cancer, Body care and body image during
and sexuality
cancerSurgery
Sexual functioning

44, 45

Body care and body image during cancer, Sexuality
and sexuality

Sexual enjoyment

46

Body care and body image during cancer, Sexuality
and sexuality

Future perspectives

43

Healing

Healing
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JMIR MEDICAL INFORMATICS
Questionnaires and their scales

Tapi Nzali et al
Questionnaire
items

cancerdusein.org

Facebook

Systemic therapy

31-34

Chemotherapy and its secondary effects

Secondary effects of treatments

Side effects

36-38

Hormone therapy and its secondary effects Chemotherapy and its secondary effects

Breast symptoms

50-53

Breast reconstruction

Symptom scales

Breast reconstruction

Radiotherapy and its secondary effects
Surgery
Arm symptoms

47-49

Breast reconstruction

Breast reconstruction

Surgery
Hair loss

35

Hair loss

Hair loss

Topics without a relationship
Complementary and alternative medicine
Mourning
Family background and breast cancer
Family members with breast cancer

Family members with breast cancer
Healing of family membera

EORTC QLQ-C30: European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30.

b

QLQ-BR23: breast cancer module.

Table 6. Distribution of documents in each topic on cancerdusein.org and Facebook.
Topic no.

cancerdusein.org (n=16,868)

Facebook (n=70,092)

n (%)

n (%)

1

978 (5.80)3294 (4.70)

2590 (3.50)

3925 (5.60)

31147 (6.80)

3785 (5.40)

4860 (5.10)

4065 (5.80)

51315 (7.80)

2804 (4.00)

6759 (4.50)

3715 (5.30)

7810 (4.80)

3014 (4.30)

8523 (3.10)

3084 (4.40)

9877 (5.20)

3645 (5.20)

10692 (4.10)3505 (5.00)

11675 (4.00)

2804 (4.00)

12523 (3.10)

2734 (3.90)

131113 (6.60)

5047 (7.20)

14692 (4.10)

3014 (4.30)

15843 (5.00)

2804 (4.00)

161063 (6.30)

2734 (3.90)

171248 (7.40)

3575 (5.10)

18

540 (3.20)

5607 (8.00)

191198 (7.10)

3432 (4.90)

20422 (2.50)3505 (5.00)

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JMIR MEDICAL INFORMATICS

Relationships Between Self-Administered
Questionnaires and Social MediaWe were able to match most of the topics from QoL
self-administered questionnaires in social media. These topics
correspond to a total of 95% (22/23) of topics in thecancerdusein.org corpus and 86% (20/23) of topics in the
Facebook corpus. These figures underline the importance ofstudying QoL, because they correspond to patients’ realconcerns. The topics that corresponded with those of the EORTCQLQ-C30 and the EORTC QLQ-BR23 questionnaires were
hair loss, work life during cancer and financial aspects,
chemotherapy and its secondary effects, breast reconstruction,
support from the patient’s family and friends, treatment period,
healing, diagnosis, breast cancer as a daily battle, body care andbody image during cancer and sexuality, hormone therapy andits secondary effects, radiotherapy and its secondary effects,
media and forum information exchange, everyday life duringcancer, search for medical information, surgery, waiting forresults of analysis, concerns, secondary effects of treatments,
interaction with nurses and doctors, anxiety and fatigue, andrelapse.

Emerging Topics in Social MediaWe also found 5 topics that are not present in QoLquestionnaires. These topics correspond to a total of 15% (3/20)of the cancerdusein.org corpus and 15% (3/20) of the Facebook
corpus. Of the 5 topics that do not appear in the questionnaires,
2 focus on patients. The emerging topics are complementary
and alternative medicine, mourning, family background andbreast cancer, family members with breast cancer, and healingof a family member. Among these 5 topics, we believe that 2of them (complementary and alternative medicine, and family
background and breast cancer) could be added to the QoL
questionnaires. The topic complementary and alternative
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Tapi Nzali et al
corresponded to a total of 3.10% (523/16,868) of thecancerdusein.org corpus. The topic family background andbreast cancer focuses on the relationships of patients with theirfamily, especially healing and grieving for a family member.
This topic corresponded to a total of 4.30% (3014/70,092) ofthe Facebook corpus. The 3 others topics are not related to QoL.
These topics deal with mourning, having family members withbreast cancer, and healing of a family member. They werediscussed by relatives of patients and not by patients.

Different Uses of Forums and Social Networks
One of the reasons that led us to use 2 data resources (social
networks and a health forum) was to discover the topics
discussed in each platform. Table 7 presents the relationships
between topics found in both social media and the percentage
distribution of messages in each topic. Of 20 topics detected byour model in the corpus forum and Facebook, we found 11
common topics in the 2 corpora. Some of them have a similarfrequency of discussion (Table 6). These topics are hair loss,
work life during cancer, support from patient’s family andfriends, treatment period, diagnosis, and family members withbreast cancer. We observed that topics such as chemotherapy
and its secondary effects, breast reconstruction, and breast
cancer as daily battle were discussed more on the forum thanon Facebook, maybe because the subject is more technical. As
Table 7 shows, we noted that the topics support from a patient’s
family and friends, body care and body image during cancer,
and sexuality were discussed more on Facebook than on theforum because of visibility to friends. In the end, the topics
discovered were quite similar. However, we observed a
difference of length in the posts. Most of the time, posts fromthe health forum were longer than posts from Facebook. Even
if the topics found in both social media were similar, messages
from the forum provided more information and were betterinterpreted than messages from Facebook.

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JMIR MEDICAL INFORMATICS

Tapi Nzali et al

Table 7. Relationships between topics found on both social media (cancerdusein.org and Facebook) with K=20 in collaboration with an expert.
Topic names

cancerdusein.org (n=16,868)

Facebook (n=70,092)

Matched to questionnaire item

Topic no.

n (%)

Topic no.

n (%)

Hair loss

1

978 (5.80)

44065 (5.80)

YesWork life during cancer and financial aspects

2590 (3.50)

83084 (4.40)

Yes

Chemotherapy and its secondary effects

31147 (6.80)

23925 (5.60)

Yes

Breast reconstruction

51315 (7.80)

93645 (5.20)

Yes

Support from patient’s family and friends

6759 (4.50)

103505 (5.00)

Yes

19

3432 (4.90)

Yes

Topics on both social media

Family members with breast cancer

10692 (4.10)

173575 (5.10)

No

Treatment period

11675 (4.00)

203505 (5.00)

Yes

Healing

131113 (6.60)

185607 (8.00)

Yes

Diagnosis

161063 (6.30)

13294 (4.70)

Yes

Breast cancer as a daily battle

171248 (7.40)

33785 (5.40)

Yes

Body care and body image during cancer, and sex- 18
uality540 (3.20)

63715 (5.30)

Yes

15

2804 (4.00)

Yes

16

2734 (3.90)

Yes

Topics on only 1 social mediaaHormone therapy and its secondary effects

4860 (5.10)

N/Aa

N/A

Yes

Radiotherapy and its secondary effects

7

810 (4.80)

N/A

N/A

Yes

Complementary and alternative medicine

8523 (3.10)

N/A

N/A

No

Media and forum information exchange

9877 (5.20)

N/A

N/A

Yes

Everyday life during cancer

12523 (3.10)

N/A

N/A

Yes

Search for medical information

14692 (4.10)

N/A

N/A

Yes

Mourning

15843 (5.00)

N/A

N/A

No

Surgery

19

1198 (7.10)

N/A

N/A

Yes

Waiting for results of analysis, concerns20

422 (2.50)

N/A

N/A

Yes

Secondary effects of treatments

N/A

N/A

5

2804 (4.00)

Yes

Family background and breast cancer

N/A

N/A

7

3014 (4.30)

NoInteraction with nurses and doctors

N/A

N/A

11

2804 (4.00)

Yes

Anxiety and fatigue

N/A

N/A

12

2734 (3.90)

Yes

Healing of member family

N/A

N/A

13

5047 (7.20)

No

Relapse

N/A

N/A

14

3014 (4.30)

Yes

N/A: not applicable.

Conclusions
In this work, we used an unsupervised learning model known
as LDA to detect the different topics on a health forum andsocial network discussed by patients. We demonstrated how weused the LDA model on patient data with relevant preprocessing
applied to 2 datasets obtained from a forum and Facebook
messages. We used MeSH as the principal resource for medical
terms and for patients’ and doctors’ vocabulary [45]. We
automatically detected relationships between topics andhttp://medinform.jmir.org/2017/3/e23/

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and the dimensions of internationally standardized
questionnaires used for breast cancer patients, which substantiate
the sound construction of such questionnaires. We detected new
emerging topics from social media that could be used tocomplete actual QoL questionnaires. Moreover, we confirmedthat social media can be an important source of information forthe study of QoL in the field of cancer.
In our ongoing work [21], we are targeting the classification ofwhole messages or text snippets with respect to the role of the
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JMIR MEDICAL INFORMATICS
narrator (patient, confidant of a patient, expert, healthprofessional) and to the location within the trajectory of care(before or after an operation, first cancer or relapse). One
potential limitation of this work was the number of topics(K=20) selected for our LDA model. This limitation may beovercome by using the number of topics for which the model
is better adjusted [47,61,62], then, first, to merge topics that areclose, and second, to find topics that could not be interpretedby humans and eliminate them. Moreover, the actual comparisonof the 2 corpora (Facebook and forum) was done manually by
the expert. A possibility is to adapt equation 1 (Figure 3) used
to align LDA topics and questionnaire items in order toautomatically compare topics extracted from the 2 corpora.
Of course, the lack of informed consent given by social media
users for data usage leads to ethical questions. In particular,

Tapi Nzali et alconfidentiality with respect to the publication of research results
is an issue (see others’ discussion and guidelines [66-68]). We
adhered to those guidelines. We have presented results with adegree of detail that does not permit conclusions on individual
users to be drawn. In the long term, we will study emotions
described by patients in their messages for each topic and makesome statistical analyses. Finally, we will use the emotion
classification system built by Abdaoui et al [69] to detectpolarity (positive, negative, or neutral), subjectivity (objective,
subjective), and feelings (joy, surprise, anger, fear, etc) of users’
messages, and we will relate this information to the detected
topics in order to determine patients’ perception of their disease.
What are the topics that frighten patients the most and that needprevention?

Acknowledgments
The 5 authors are justifiably credited with authorship, according to the authorship criteria. In particular: MDTN: acquisition ofdata, analysis and interpretation of data, drafting of the methods, final approval; TO: conception, analysis and interpretation ofdata, drafting of the manuscript, final approval; CL: conception, analysis, drafting of the manuscript, final approval; SB: acquisitionof data, critical revision of manuscript, final approval; CM: interpretation of data, critical revision of manuscript, final approval.
This work was supported by the ANR SIFR (Semantic Indexing of French Biomedical Data Resources) and by a grant from the
French Public Health Research Institute (http://www.iresp.net) under the 2012 call for projects as part of the 2009-2013 Cancer
Plan.

Conflicts of Interest
None declared.

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Abbreviations
EORTC: European Organization for Research and Treatment of Cancer
INCA: Institut National du Cancer
LDA: latent Dirichlet allocation
MeSH: Medical Subject Headings
QLQ-C30: Quality of Life Questionnaire Core 30
QoL: quality of life

Edited by G Eysenbach; submitted 05.04.17; peer-reviewed by K Zhang, D Linares, S Tuo; comments to author 17.05.17; revised
version received 16.06.17; accepted 17.06.17; published 31.07.17
Please cite as:
Tapi Nzali MD, Bringay S, Lavergne C, Mollevi C, Opitz T
What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer
JMIR Med Inform 2017;5(3):e23
URL: http://medinform.jmir.org/2017/3/e23/
doi: 10.2196/medinform.7779
PMID: 28760725©Mike Donald Tapi Nzali, Sandra Bringay, Christian Lavergne, Caroline Mollevi, Thomas Opitz. Originally published in JMIR
Medical Informatics (http://medinform.jmir.org), 31.07.2017. This is an open-access article distributed under the terms of theCreative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
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