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Original Paper

Novel Approach to Cluster Patient-Generated Data Into Actionable
Topics: Case Study of a Web-Based Breast Cancer Forum
Josette Jones1, PhD, RN; Meeta Pradhan2*, PhD; Masoud Hosseini1, PhD; Anand Kulanthaivel1*, MIS; Mahmood
Hosseini3, BS
1

Health Informatics, BioHealth Informatics Department, Indiana University, Indianapolis, Indianapolis, IN, United States

2

Indiana Biosciences Research Institute, Indianapolis, IN, United States

3

Purdue School of Science and Technology, Purdue University, Indianapolis, Indianapolis, IN, United States*these authors contributed equally

Corresponding Author:
Josette Jones, PhD, RN
Health Informatics
BioHealth Informatics Department
Indiana University, Indianapolis
535 West Michigan Street
Indianapolis, IN, 46202
United States
Phone: 1 3172748059
Email: jofjons@iupui.edu

Abstract
Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers
to share information about their health and well-being. Information shared through social media contains not only medical
information but also valuable information about how the survivors manage disease and recovery in the context of daily life.
Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online
breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of
disease management and recovery.
Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum
board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings.
Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear
regression (MLR) analysis to detect highly correlated topics among the different website forums.
Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings
into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into
4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, andFamily & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection ofactionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated
that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant.
Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable
in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers
discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social
media to inform the clinical workflow with regards to the impact of recovery on daily life.
(JMIR Med Inform 2018;6(4):e45) doi: 10.2196/medinform.9162
KEYWORDS
data interpretation; natural language processing; patient-generated information; social media; statistical analysis; infodemiology

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Introduction

Methods

Health care is currently undergoing transformation bycapitalizing on information technology and patient-consumer
engagement and activation through health information
technology such as patient portals and mHealth apps. Consumer
engagement is assumed to strengthen providers’ abilities totailor their care to the consumers’ needs, preferences, andabilities. The increasing use of smartphones, mobile apps, andremote monitoring devices, coupled with providers’deploymentsof electronic health records, patient portals, and secure
messaging, offers innovative ways to connect patients andproviders and to strengthen consumers’ engagement in theirhealth and well-being [1]. In addition, health consumers haveembraced social media, enabling them to share and discuss how
they manage their health and well-being with others with similar
health issues. These social media and mHealth apps generate
important data outside the health care settings and, when sharedwith providers, expand the depth, breadth, and continuity of
information available to optimize health care and outcomes.

Overview

Despite the proliferation of social media use, such as blogs andforums, little is known about the scope and quality of
information shared, or the purposes that social media sites servefor consumer decisional and support needs [2]. Social media
retains large amounts of valuable information about consumers’contextual and environmental (day-to-day) factors while
managing their health and well-being; such issues form a major
foundation of human health. However, analyzing those free-text
data to discover these hidden aspects of health consumers’ lives
and understand their health information needs beyond thoseroutinely addressed by health care providers is challenging [3].
This study explores approaches for analyzing the social media
data and extract potential valuable information on managing
health and well-being beyond the context of health care. As it
is known that breast cancer patients and survivors often joinsocial media to fulfill their information needs and discuss their
daily challenges and concerns, all or not related to health [4],using those venues is apparent. Some concerns might not beshared with health care providers for many reasons. For
example, patients might think it is not necessary to discuss the
topic, may feel embarrassed about the issue, or they do not evenknow there is a problem [5,6]. Hence, we explored ways todiscover issues that are not commonly shared but are important
for the overall health and well-being.

First, manual topic categorization was performed using
qualitative content analysis (QCA) of each individual forum
board. Second, we requested permission from theBreastcancer.org Community for a more in-depth analysis of
the postings. In addition, natural language processing (NLP)
and statistical modeling approach were used to cluster >4 million
postings into manageable topics. Finally, topic modeling was
performed using open source software, followed by multiple
linear regression (MLR) analysis to detect highly correlated
topics among the different website forums. The methodology
is outlined in Figure 1.

Manual Categorization of Posts
A Google search was performed for breast cancer forum
websites. Selection criteria were active websites (having postsin the week of search) and in the English language. Each website
must have at least 5000 members or have a minimum of 50,000
posts in total, and the posts on the site must be organized into
categories. Among the resulting 20 websites, 5 were included
on the basis of the selection criteria. These 5 remaining websites
contained 4,901,516 posts organized in 211 forums (Table 1).
The forum posts were further manually analyzed for consensus
among the team members.
Team members were assigned to review the titles of 211 forums
across the 5 forum websites and organize them into 4 main
top-level categories and 16 subcategories correlating to several
domains from the report on the social determinants of healthpublished by the Institute of Medicine [12]. The quantities of
posts belonging to each category and subcategory were
calculated by the forum that the posts belonged to. The 3 mostdominant subcategories across all websites were as follows:
Treatments(1.49/4.90
million,30.5%,
posts),
Diagnosed–Psychosocial Support–Similar Patients (1.34/4.90
million, 27.3%, posts), and Diagnosed–Psychosocial
Support–Life (0.83/4.90 million, 16.9%, posts). After the posts
were categorized, the research team iteratively validated (witha random sample of 20 posts from each forum) and consolidated
the initial categorization, assuring the quality and correctnessof the method.

Figure 1. Overview of the methods used to analyze the study content.

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Table 1. Breast cancer websites explored.

aWebsite name

Site URL

Country Forums

Threads Posts

Members

Breastcancer.org Community [7]community.breastcancer.org

USa

80

121,688 3,608,324 153,620

Breast Cancer Care [8]forum.breastcancercare.co.uk

UKb

54

36,949782,486 N/Ac

Susan G Komen Foundation: Message Board [9] apps.komen.org/Forums

US24

44,175354,592 26,883

Triple Negative Breast Cancer: Forums [10]forum.tnbcfoundation.org

US

17

9641

100,706 123,427

No Surrender Breast Cancer Foundation [11]nosurrenderbreastcancersupportforum.com US

36

2443

55,4985549

US: United States.

b

UK: United Kingdom.

c

N/A: not applicable.

Data Extraction, Natural Language Processing, andStatistical Modeling
Data from a public breast cancer internet discussion forum wereextracted, cleaned, and processed; multiple approaches merging
NLP with statistical modeling were implemented for knowledge
discovery. In addition, off-the-shelf products were used todevelop and streamline the analytical approach to cluster
most-occurring topics of discussions. The methodology
developed revealed several topics that may be of importancefor care planning and, thus, need to be incorporated in theelectronic health record. In addition, advanced text mining will
be a foundation for predictive modeling of consumers’ healthinformation needs and provide interactive solutions.

Extracting (Scraping) Forum Data
Postings from the Breastcancer.org Community website wereselected for further analyses, as this website contained thehighest number of total posts (3.61/4.90 million 73.6%, posts
across all 5 websites selected in this study). Permission was
obtained from the Web administrators to download and analyze
all the data logged in the site.
The Breastcancer.org Community site includes 80 main forums
organized by the site administrators into 9 sections. Users
self-select in which forum, and thus in which section, a postthat they make will go. To capture information within the forum
posts, an in-house scraping tool in the PHP Hypertext
Preprocessor language was developed by a team member.
Forum metadata along with the actual posts were extracted; thetext within the different posts was aggregated into 80 text files
each corresponding to a forum. The files were named based on
the forum ID number. The data were saved in the JavaScript
Object Notation format. Multimedia Appendix 1 shows theforum names along with the number of threads and posts ineach forum.

Applying Topic Modeling
Topic models provide a simple way to analyze large volumesof unlabeled text. A “topic” consists of a cluster of words thatfrequently occur together. Using contextual clues, topic models
can connect words with similar meanings and distinguishbetween uses of words with multiple meanings. One of theleading approaches used for topic modeling is Latent Dirichlet
allocation (LDA), which is one of the most popular methods in

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NLP [13]. LDA represents a document as a distribution of“topics,” where a topic is itself a distribution over words (andmay or may not be similar to a forum topic). Looping througheach word in every document, the LDA algorithm assigns every
word to a temporary topic in a semirandom manner anditeratively updates topic assignments. For each word, its topic
assignment is subsequently updated based on 2 criteria as
follows: (1) the prevalence of the word across all topics and (2)the prevalence of the topics within the documents.
The Machine Learning Language Toolkit (MALLET) open
source tool (University of Massachusetts, Amherst, MA, USA)[14] was used to execute the LDA algorithm on the data to
extract the main topics. MALLET is a Java-based tool developed
at the University of Massachusetts Amherst, which is used forthe analysis of data in a textual format such as documentclassification, clustering, topic modeling, information extraction,and other machine learning apps. After scraping the forum dataand saving into 80 files representing each forum, the files were
imported into the MALLET tool. MALLET generates two
tab-delimited text files as a result of algorithm execution. One
file contains the topic ID, and the words related to that topic(aka the topic keywords; Table 2).
MALLET was run iteratively, customized to generate 15 topics,
20 topics, and 30 topics, respectively. Topic labels were addedby consensus of the research team based on the semantics of
the word cluster. Some topic labels in different sets of topics
were identical based on the semantic similarity, but the topic
words and strength are different for each of the 3 sets generated.
No new topics were generated at the third iteration; theMALLET categorization of 30 word baskets was used for further
analysis.
For each iteration and each file, the topic composition andcorresponding LDA strength were computed, providing us away to infer the latent structure of the text file. The resulting
output is a topic ID-by-text file matrix known as a file-featureset (Table 3). The first column shows the name of the file; therest of the columns are best considered as (topic-ID,
topic-strength) pairs. For example, it is noted that file F100 has
a Topic 12 strength of 0.275 (27.5%). For each document, there
are as many of these pairs as there are topics, although only the
top 5 topics for each file and the first 4 files are shown forbrevity.

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Table 2. The partial table of topics generated by Machine Learning Language Toolkit in the 30-topic model, with interpretations (the list goes on up
to the 30th topic; only 3 are shown for brevity).
Machine Learning Language Topic label
Toolkit topic identifier

Topic keywords

1

Diagnostic testing and waiting for results

breast biopsy cancer lump results ultrasound benign surgeon mammogram
doctor mri waiting back mammo good radiologist feel pain left i'm

2Side effects of inflammation and its treatment

breast ibc skin symptoms pain rash red cancer nipple biopsy infection diagnosed antibiotics swollen treatment left specialist redness swelling lymph

3

Positive results after recurrence

chemo stage years cancer treatment nodes onc tumor triple negative taxol
positive rads year diagnosed node recurrence congratulations lymph radiation

Table 3. A portion of the file-feature set generated by Machine Learning Language Toolkit software (the list goes on up to the 80th file and 30th topic;
values were truncated for brevity of display).

aFile identifiera

Topic IDb Strengthc

Topic ID

Strength

Topic ID

Strength

Topic ID

Strength

Topic ID

Strength

F100

12

0.275

18

0.269

2

0.251

5

0.06

7

0.053

F102

2

0.542

18

0.136

7

0.087

12

0.056

1

0.04

F104

2

0.315

14

0.118

1

0.104

7

0.09

20

0.043

F105

2

0.295

11

0.25

6

0.213

7

0.067

14

0.042

Scraped forum file.

b

Topic identifier: Machine Learning Language Toolkit-generated topics.

c

Weight of topic in the file.

Statistical Analyses
The output from MALLET assigned weight scores (ie,topic-strength) to each topic-ID within each file. Statisticalanalysis was carried out (1) to understand the similarity acrossthe feature sets and files and (2) to identify the topics that are
most relevant to patients with breast cancer. Euclidean Distance
Similarity Measures (EDSM) were computed to evaluate thesimilarity across the files based on their weight scores for each
topic. Equation (1) is an example of how each file and its feature
vectors were assigned, using file F100 as an example.
F100 = [Topic6(.33), Topic1(.28), Topic9(.20),
Topic2(.08), Topic0(.05), Topic3(.04), Topic12(.01),
Topic4(.01), Topic n(weight m)…] [1]
The EDSM between all potential file pairs were computed on
the basis of Equation (2):Where i and j are identifiers for each file pair, k is the total
number of topics in the dataset (ie, 15, 20, or 30 topics), and x
is the topic weight score in each file.
To identify the most relevant topics in the dataset, an MLR
analysis was performed on all files. The MLR analysis wascomputed using the R Statistical Package [15]. The MLR
analysis identified the topics that seem to be most relevant
within and across the forums in the study. The equation model(Equation 3) for the MLR used is:

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Where topic1, topic2 are the weight scores of the topics in thefiles; εi is the error in the model; β0 is the intercept; β1, β2 are
the coefficients for the topic1, topic2, respectively, computedby the model; and yi is the outcome (dependent variable) for
each file i.

Results
Manual Categorization of Posts
Among the 5 sites studied, Breastcancer.org Community
presented the majority of the total post volume. From all
websites analyzed for post counts, 73.6% (3.61/4.90 million)of posts were from Breastcancer.org Community; hence, we
selected this site for further exploration.

Manual Categorization
Initially, the research team performed via QCA a manual
categorization of topics discussed in the 5 selected public
websites. The popularity distribution of the manually generated
categories, as discussed in the Methods section, was assessedby the number of posts made in the forums. For example, thequalitatively generated categories Diagnosed—Psychosocial—
Similar Patients have an overall popularity of 41,972 posts. The
forum categorization was either general or granular dependingon the forum structure. The distribution of qualitative categories
across the threads on the Breastcancer.org Community website
is visualized in the figures below by frequencies of category
popularity (Figure 2); 20 of our QCA-generated categories
mapped to forums on breastcancer.org. The x-axis shows the
QCA-assigned category names.

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Figure 2. Distribution of qualitative content analysis-generated categories according to the number of forum threads that each manual category possesses.
DX: Diagnosed; TLD: Top-Level Domain; NDC: Not Diagnosed but Concerned.

Data Extraction, Natural Language Processing, andStatistical Modeling
Data Extraction
The data for all 80 forums on the Breastcancer.org Community
website were successfully extracted into 80 files, each containing
all communications posted over its respective forum.

Natural Language Processing: Topic Modeling
As mentioned earlier, exhaustion was reached at baskets of 30
cooccurring words. The remainder of the analyses will be only
for these topics. All machine-generated topics were assigned
topic labels based on the semantics of the word cluster andvalidated by a domain expert (clinical); the topic ID was equatedwith the term key. MALLET assigned an LDA strength to each
topic indicating its overall dominance across all forum files thatwere analyzed. Two example topics, IDs #8 and #29, are listed
in Table 4 (below), along with the authors’ labels for these
topics. Multimedia Appendix 2 provides a full list of generated
topics from this model as well as the authors’ semanticinterpretations. Each file represents the text of one forum, andtopic-strength pairs for the strongest five topics per MALLETLDA analysis of that file are found to the right of the file’s ID.

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For any file, the strength across all 30 topics will always add
up to 1.00.
MALLET also correlated the topic relationship strengths
between all files based on their topics. Strengths assigned to
document-topic pairs by MALLET ranged from almost zero(<0.000001) up to 0.796. The maximum theoretical possible
strength for a single file-topic pair would be 1.00. Multimedia
Appendix 3 provides a list of the top 5 correlated topics of each
file.

Statistical Analysis: Euclidean Distance Similarity
Measures
EDSM were calculated to find the similarity between files.
Figure 3 shows the file-file similarity matrix and a subset of thesimilarity matrix. The files are mirrored across both axes andordered by their alphabetical file name (with F100 being firstand F99 being last). Darker cells indicate that the files weremore similar. File 109 has a similarity measure of 0.89 with file104; similarly, file 108 has a similarity measure of 0.78 withfile 104.
Table 5 illustrates the similarity measures among the file pairs
with a similarity score ≥0.8.

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Table 4. Topics #8 and #29 with Latent Dirichlet allocation strengths author topic label interpretations.
Topic identifier

Latent Dirichlet allocation strength Topic words

Authors’ topic label

81.38724cancer chemo years feel life family mom time support
things breast people treatment don’t husband care friends
diagnosed talk mother

Hope, love, family, and friends

29

0.19954

hair book pink survivor happy deb health country president Daily living and breast cancer
shirley obama congratulations cats article eye mammo fumi
beth beautiful vote

Figure 3. File-file similarity matrix.

Table 5. Top scored file-file similarity measures.
File identifier

Associated files (similarity score)

F102

F133 (0.85), F144 (0.91), F152 (0.97), F116 (0.95)

F104

F109 (0.89), F142 (0.81), F150 (0.82), F27 (0.89)

F108

F132 (0.94), F137 (0.86), F145 (0.97), F5 (0.90), F71 (0.93), F88 (0.86), F96 (0.97)

F109

F104 (0.89), F142 (0.89), F127 (0.85)

F110

F26 (0.80)

F111

F132 (0.8), F68 (0.87)

F112

F47 (0.92), F93 (0.94)

F113

F139 (0.89), F55 (0.87)

F133

F102 (0.85), F135 (0.86), F145 (0.90), F5 (0.87), F71 (0.94), F96 (0.94), F88 (0.87)

A cursory review of the files with high similarity also revealed
clinical relevance and connection. For example, files F108, 132,
and 145, while in different discussion categories on the website,
all discuss the diagnosis, treatment, and potential side effects
from the treatments and also discuss living with different stages
and types of breast cancer. In addition, F96 has a high similarity(97%) with F108, which is devoted to the breast cancer type

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known as invasive ductal carcinoma. F112 discusses more
specific genetic risks of breast cancer (BRCA1 or BRCA2
positive), while F47 (similarity 92%) chats about more generalrisks. F93 at first glance seems not related (Comments,
Suggestions, Feature Requests), but reading the postings
revealed the need for more information and social support forusers who find out that they are at risk for breast cancer.

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Figure 4. Topic-topic similarity matrix.

Figure 4 shows 4 clusters of highly correlated computationallymodeled topics. Each cluster is labeled with a letter, and topics
are labeled as keys. The topics with their consensus labels basedon the semantic meaning of the word baskets are as follows:

•
•
•
•

Cluster A: Symptoms & Diagnosis

•
•
•

•
•
•
•
•
•
•

Topic1: Diagnostic testing and waiting for result
Topic 7: Genetic risk and testing
Topic 30: Symptoms and diagnosis of recurrence
Topic 3: Positive results after recurrence
Topic 26: Positive results after treatment for recurrence
Topic 9: Diagnostic and treatment observation forrecurrence
Topic 14: Medical drug treatment and long-term effects

Topic 21: Lingering side effects while in remission
Topic27: Surgical reconstruction during remission
Topic 5: Prognosis about relapse or recurrence
Topic 25: Support from caregivers and medical team forrecovery long term
Topic 17: Nutrition and supplements
Topic 2: Side effect of inflammation and its treatment
Topic 10: Radiation side effects and duration of the effects.

Cluster C: Financial•••

Topic 24: Financial issues over time
Topic 11: Forum-related discussion for support from people
in similar circumstances
Topic 13: Looking for clinical research and trials

Cluster B: Treatment

Cluster D: Friends & Family

•
•
•
•
•
•

•
•
•
•
•
•

Topic18: Chemotherapy side effects and change of treatment
Topic 22: General feeling over time
Topic 23: Medical or drug treatment and side effects
Topic15: Physical activities during and after chemo
Topic 20: Side effects of breast cancer treatment
Topic16: Surgical treatments while in remission

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Topic 4: Friends and fun
Topic 29: Everyday life and breast cancer
Topic 28: Spirituality and religion
Topic 19: feeling back to normal
Topic 8: Hope, love, family, and friends
Topic 12: Feeling positive and support
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Table 6. Most significant topics identified via multiple linear regression analysis.
Topic identifier

Topic label

Akaike information criterion values

21

Lingering side effects while in remission−642.7518

Chemotherapy side effects and change of treatment−641.98

10

Radiation and side effects−633.17

7

Genetic risk and testing−620.4125

Support from caregiver and medical team for recovery long term

−571.78

11

Looking for support from people in similar circumstances−412.32

As can be deduced from the semantic labeling, each cluster
describes a theme: cluster A is related to risk factors, diagnosis,
and potential risk of recurrence, whereas cluster B describes
different treatments and their side effects in the short and longterm. Cluster C and D are less clinically and more oriented topatient contextual factors (ie, those that are typicallyascertainable only outside of the clinic encounters).

Statistical Analyses: Multiple Linear Regression Analysis
Finally, MLR analysis was performed to identify the mostsignificant topics (keys) across the 4 abovementioned clusters.
The topics were arranged in a descending order based on the
Akaike information criterion value, the most appropriate
measure for the methodology. The most significant topics
identified by the model were: Topic21> Topic18> Topic10>
Topic7> Topic25> Topic11. Table 6 reports the most significant
topics discussed among the forum participants, along with therespective topic labels assigned by the authors.

Discussion
Principal Findings
It is well known that many users share information online daily.
Forum posts, blogs, or other social media activity reveal a richdiary of everyday life. Health information is revealed explicitly
when an individual communicates about their well-being or
when they ask for guidance, information on a very specifichealth issue, treatment, and other related topics. Our goal was
to explore a method to enhance our ability to collect andinterpret information from those social media sources. Our
methodology allowed us to organize 4 million plus postings
into 30 topics, consequently clustered into 4 groups.
The popularity of QCA-generated categories (as measured by
the number of posts in their associated forums) showed alogarithmic-linear (log-lin) distribution, strongly suggestingthat a few QCA-generated categories are disproportionately
gravitated toward user self-selection, while most topics receive
comparatively little attention. Moreover, it is of great interest
that topic modeling analysis via MALLET showed that theoverall LDA strengths of each topic among the forum documents(as seen in Figure 3) also followed a log-lin type of distribution,
allowing for the same type of conclusion in objectively
quantifying content with regards to the MALLET-generated
topics.
In addition, a modest level of correlation was observed between
the strongest (via MLR analysis) MALLET topics and thestrength (by user posting) of manual QCA-generated categories.
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Topic 11, looking for support from people in similar
circumstances, is almost semantically identical to the
QCA-generated category of Diagnosed—Psychosocial—Similar
Patients. The latter category encompassed 34.4%(41,972/121,688 of all threads on the Breastcancer.org
Community website. Topics 10 and 18 (Radiation and side
effects and Chemotherapy side effects and change of treatments,
respectively), meanwhile, correlate in general to the manually
generated category of Diagnosed—Treatment (top-leveldomain), which covered 38,698 threads on the site. Topic 7(genetic risk and testing) is semantically similar to the
QCA-generated category of Not Diagnosed But
Concerned—Testing, noted in 4519 site user threads.
The computationally assigned importance of Topic 11 whencombined with its equally significant manually generated
category correlate demonstrates the need for health personnel
to take into account the contextual (nonclinical) factors unlikely
to be captured in conventional medical documentation and notsupported by conventional clinic-based information technology
resources. In particular, a greater emphasis on
information-mediated psychosocial interventions is supportedby the results of this research.
The computational topic modeling analysis via MALLET also
demonstrated topics that did not arise via manual category
generation. These topics, in particular Topic 21 (lingering side
effects while in remission) and Topic 25 (support from caregiverand medical team for recovery long term), mirror breast cancer
survivorship instead of the disorder itself. Therefore, it issuggested that computational topic modeling software such as
MALLET is useful in future research on large bodies ofpatient-generated text and can generate topics similar in quality
to those generated by expert QCA; furthermore, this type ofsoftware can detect significant but hidden topics (such as socialand daily living issues dealt with during survivorship) that are
not otherwise detectable when only the forum labels given bya site are analyzed qualitatively.
Visual analysis of the file-file (ie, forum-forum) similarity
matrix (see Figure 4) shows a particular concentration of similar
files across the diagonal axis, indicating that files numberedwith close numbers tended to be more similar in content. Thisobservation actually does strengthen the case for using
computational topic modeling software such as MALLET
because closely numbered files in the study at hand tended tooriginate from forums that resided in identical categories on theBreastcancer.org Community website. Similarly, topics thatclosely correlated with each other were noted to have clinically
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significant correlates. It is important to note that many of these
correlates may not have been intuitive at face value but weremore explainable with the clinical expertise.

patient records and potentially even allow for the capability for
analyzing patient narratives in a longitudinal (time-wise)
fashion.

Overall, the research team was able to gain significant insight
into the daily lives, clinical and otherwise, of patients affectedby breast cancer; the onus to support survivors of breast cancer
was also revealed. Furthermore, the research performed
generated significant support for the use of computational topic
modeling software such as MALLET to analyze
patient-generated information for nonclinical issues revealed
by patients with breast cancer over relevant disease-specificonline forums.

Detailed analysis of similarity measures between files andclustering methods is an important part of potential future
research and will require thorough analysis by clinical andpatient health experts. This process is very time consuming andis out of the scope of this study as our goal in this work is topresent a method for modeling a data in a meaningful format.
Future work will focus more on further analysis of data toidentify hidden relationships between files and topics that mightreveal hidden aspects of breast cancer patients’ challenges intheir real life.

Limitations and Future Considerations
The data could be better annotated in metadata-facilitatedcontext as opposed to being in a purely free-text format; thegranularity of the ontology in which the data are stored can be
improved in future research. In particular, having the posts
traceable to unique anonymized users would be of assistance.
To achieve this granularity, forums in the future can be scraped
in a manner that preserves the HTML source code of theircontent; structured information could be then extracted fromthe HTML. The granularity of time, if extractable from theHTML, could potentially facilitate the generation of individual

Conclusions
The importance of patient-generated data (including
patient-generated information via online communications) is
growing among scholars because of their value in identifying
hidden aspects of patients’ challenges and concerns. This study
provides a reasonable amount of insight into the areas of interestor concern that patients with breast cancer discuss in social
media and may need to be addressed to optimize patient disease
and health management.

Acknowledgments
The authors would like to acknowledge Breastcancer.org Community (website) as well as the graduate students from the BioHealth
Informatics Department who provided support and input in different aspects of the work. This research received no specific grantfrom any funding agency in public, commercial, or not-for-profit sectors.

Conflicts of Interest
None declared.

Multimedia Appendix 1
The 80 forums and their groupings on the Breastcancer.org Community website.
[PDF File (Adobe PDF File), 112KB-Multimedia Appendix 1]

Multimedia Appendix 2
File-feature set.
[PDF File (Adobe PDF File), 107KB-Multimedia Appendix 2]

Multimedia Appendix 3
List of topics generated by MALLET in the 30 topic model.
[PDF File (Adobe PDF File), 117KB-Multimedia Appendix 3]

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Abbreviations
EDSM: Euclidean Distance Similarity Measures
LDA: Latent Dirichlet allocation
MALLET: Machine Learning Language Toolkit
MLR: multiple linear regression
NLP: natural language processing
QCA: qualitative content analysis

Edited by G Eysenbach; submitted 11.10.17; peer-reviewed by D Attai, D Hafez; comments to author 23.11.17; revised version received
14.03.18; accepted 21.06.18; published 29.11.18
Please cite as:
Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M
Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer ForumJMIR Med Inform 2018;6(4):e45
URL: http://medinform.jmir.org/2018/4/e45/
doi: 10.2196/medinform.9162
PMID: 30497991©Josette Jones, Meeta Pradhan, Masoud Hosseini, Anand Kulanthaivel, Mahmood Hosseini. Originally published in JMIR
Medical Informatics (http://medinform.jmir.org), 29.11.2018. 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
complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright andlicense information must be included.

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