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Kamba et al

Original Paper

Medical Needs Extraction for Breast Cancer Patients from
Question and Answer Services: Natural Language
Processing-Based Approach
Masaru Kamba1, MSc; Masae Manabe1, MEd; Shoko Wakamiya1, PhD; Shuntaro Yada1, PhD; Eiji Aramaki1, PhD;
Satomi Odani2, MPH; Isao Miyashiro2, MD, PhD
1Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan

2

Cancer Control Center, Osaka International Cancer Institute, Osaka, Japan

Corresponding Author:
Eiji Aramaki, PhD
Division of Information Science
Graduate School of Science and Technology
Nara Institute of Science and Technology
8916-5 Takayama-cho
IkomaNara, 630 0192
Japan
Phone: 81 743 72 5111
Email: aramaki@is.naist.jp

Abstract
Background: A large number of patient narratives are available on various web services. As for web question and answer
services, patient questions often relate to medical needs, and we expect these questions to provide clues for a better understandingof patients’ medical needs.
Objective: This study aimed to extract patients’ needs and classify them into thematic categories. Clarifying patient needs isthe first step in solving social issues that patients with cancer encounter.
Methods: For this study, we used patient question texts containing the key phrase “breast cancer,“ available at the Yahoo! Japanquestion and answer service, Yahoo! Chiebukuro, which contains over 60,000 questions on cancer. First, we converted the question
text into a vector representation. Next, the relevance between patient needs and existing cancer needs categories was calculated
based on cosine similarity.
Results: The proportion of correct classifications in our proposed method was approximately 70%. Considering the results ofclassifying questions, we found the variation and the number of needs.
Conclusions: We created 3 corpora to classify the problems of patients with cancer. The proposed method was able to classify
the problems considering the question text. Moreover, as an application example, the question text that included the side effectsignaling of drugs and the unmet needs of cancer patients could be extracted. Revealing these needs is important to fulfill the
medical needs of patients with cancer.
(JMIR Cancer 2021;7(4):e32005) doi: 10.2196/32005
KEYWORDS
natural language processing; internet use; patient generated health data; neoplasms

Introduction
Background
Patients with cancer have many medical needs. These needs arediverse and not necessarily communicated to doctors, nurses,
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and other medical staff. A database of their problems is needed
to determine which patients experience problems or have unmet
needs and to what extent.
Such a database does exist in Japan, the “cancer problem
classification” (CPC), and is maintained by the Shizuoka Cancer
Center. It was created by collecting and categorizing the claims
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of cancer survivors through a nationwide survey into 4categories. The CPC has systematized the worries and burdensof patients with cancer surveyed through telephone consultations
and other means, with 7855 participants in 2003 and 4054 in2013 [1]. However, this process was conducted manually by
experts, and making a new one would be costly andtime-consuming. With the recent exponential growth of theinternet, a vast number of illness-related problems have alreadybeen accumulated in Japan [2], where blogs are actively written.
As of July 2021, TOBYO [3] has the largest collection of diaries
and blogs in Japan dedicated to battling diseases (approximately
63,000 of such diaries and blogs), covering some 1500
conditions. Among them, the number of blogs on breast cancer,
the treatment of which tends to be prolonged, is particularly
large, accounting for over 10% (approximately 6900) of blogs.
Furthermore, Yahoo! Japan’s question and answer (YJQA)
service, commonly called Chiebukuro [4], is one of Japan's
leading question and answer (QA) services, containing
approximately 60,000 questions that include the key phrase“breast cancer.” Thus, a vast archive of patient claims has
already been created on the internet.
In this context, many recent studies have utilized accumulated
information [5-9]. For example, Rosenblum and Yom-Tov [5]investigated how people search for information related toattention-deficit/hyperactivity disorder using the Microsoft Bing
search engine [10] and Yahoo! Answers, a web QA site. Parket al [6] investigated the use of medical concepts regarding
diabetes from the textual data of blogs and QA sites, whereas
Yom-Tov and Gabrilovich [7] investigated the side effects ofmedications from web search queries. Tsuya et al [8]demonstrated that cancer patients share information about theirdiseases, including diagnosis, symptoms, and treatments via
Twitter [11], and Hong [9] explored whether patients could
accurately and adequately express their information needs on
Chinese health QA websites. Thus, using patients’ claims on
the web can provide a qualitative and timely understanding ofneeds from the patient’s perspective and be considered a typeof patient-reported outcome, which may help transform healthcare in terms of patient-centered care [12,13].
However, there are some limitations to using the accumulated
information, the biggest problem being the difficulty in
examining a large amount of data. Because there is no existing

Kamba et alclassification, similar to the CPC, we can only gather a limited
amount of information on side effects, for example, by manuallyprocessing the data. Therefore, the automatic classification of
text data is essential.

Objectives
This study aimed to extract the needs of patients with breast
cancer from the YJQA data and classify them into CPC
categories. We adopted the fourth-level CPC categories
described above for the classification of patient needs. In the
CPC’s first-level categories, the problem granularity is coarse,
and it is difficult to understand specific issues. For example,
while the CPC's first-level category is outpatient, thecorresponding fourth-level categories are “1.1.1.1. Difficultyin obtaining information to select a hospital or doctor” and“1.1.1.2. Difficulty in hospital selection.” Therefore, this study
attempted to classify the fourth-level categories to grasp
patients’ problems more concretely.

Methods
Materials
This study built a data set of 7993 questions submitted to the
YJQA between January 1, 2018, and July 31, 2020. The CPC
has been systematized to use the problems and burdens of cancer
patients, consisting of 16 first-level categories and 631
fourth-level categories. This study utilized 2 corpora: the CPC
corpus and the YJQA corpus, for training.
The CPC corpus is a large collection of pairs of cancer
survivors’ worries and their labels. The label consists of the
CPC category code and the CPC category name (hereafter, bothare collectively referred to as CPC categories), obtained fromthe CPC database [1]. Unless otherwise noted, the CPC
categories represent fourth-level categories. An example fromthe CPC corpus is presented in Textbox 1.
The YJQA corpus is a labeled corpus of 1000 randomly selected
questions on breast cancer posted to the YJQA from January 1,
2018, to June 9, 2020. Because multiple different worries are
possible, each question is assigned manually to up to 3 different
CPC categories. An example from the YJQA corpus is presented
in Textbox 2.

Textbox 1. CPC category code, name, and cancer survivors’ worries.
CPC category code: 1.1.1.1
CPC category name: Difficulty in obtaining information for selecting hospitals and doctors.
Cancer survivors’ worries: I was worried because I had to make decisions based on my limited knowledge and emotions, without any information
or indicators to judge whether the hospital's policies and techniques were accurate, especially whether my doctor was trustworthy.
Note: CPC refers to the “cancer problem classification.”

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Textbox 2. CPC category code, name, and questions in YJQA.
CPC category code: 1.1.1.1.
CPC category name: Difficulty in obtaining information for selecting hospitals and doctors.
Question in YJQA: Choosing a hospital for breast cancer treatment. I'm wondering if I'm making a mistake in choosing the first hospital. Is there
any problem in choosing the university hospital that is closest to my house?
CPC category code: 3.2.2.1./16.3.2.1.
CPC category name: I'm worried about finding out the test results/concerns regarding suspicion of cancer (other)
Question in YJQA: I had a breast cancer screening and had to be retested for a suspected breast mass. My mother had breast cancer. I will have amammogram next month. Is the chance of getting breast cancer high? I am very scared and worried.
Note: CPC refers to the “cancer problem classification,” and YJQA refers to Yahoo! Japan’s question and answer service.

We assigned CPC categories to 456 of the 1000 cases, while
the remaining 546 cases had no corresponding CPC categories.
Thus, the total number of cumulatively classified questions was
661, which were assigned to 133 CPC categories. Table 1
summarizes the most frequent categories, up to the 10th (top10), regarding the number of questions classified. For example,
the most frequent category was “worrying about cancer withsubjective symptoms,” with 24.2% of the labeled data falling

into this category. Moreover, the category “difficulty in
expressing questions and concerns to doctors” was included in
the top 10 categories, suggesting that people submitted questions
to the YJQA because they had difficulty expressing theirconcerns to their doctors.
Of the 7993 questions submitted to the YJQA, 6993 were usedas the YJQA corpus data classified using CPC categories,
excluding the 1000 labeled questions (training data).

Table 1. Results of manual classification of YJQA questions (top 10 categories).
CPC category code

CPC category namen, %16.3.1.1.

Worrying about cancer with subjective symptoms

160 (24.2)

16.2.1.1.

Matters related to cancer screening

85 (24.2)

12.2.4.1.

Anxiety due to lack of knowledge about cancer

42 (12.9)

16.3.2.1.

Concerns regarding suspicion of cancer (other)39 (6.4)

9.1.2.2.

Difficulty in asking questions or expressing concerns to the doctor

17 (2.6)

3.2.2.2.

Worrying about the results and their trends

17 (2.6)

12.1.1.1.

Anxiety about the possibility of recurrence or metastasis

14 (2.1)

3.2.1.6.

Concerns about undergoing tests (other)11 (1.7)

3.1.1.1.

Uncertainty about treatment options

10 (1.5)

3.2.2.3.

Issues related to receiving tests (other)

9 (1.4)

Classification Algorithm
Our classification algorithm consists of the following steps:
1.

2.
3.

Preprocessing: Convert the 2 corpora (CPC corpus and
YJQA corpus) into term frequency (TF)-inverse document
frequency (IDF)-weighted word vectors.
STEP1: Given an unknown problem, convert the problem
into TF-IDF-weighted word vectors.
STEP2: Classify the target problem into the most relevant
CPC category based on cosine similarity between the target
problem’s vector from STEP1 and vectors from the 2
corpora.

Here, we extract nouns, verbs, and adjectives using themorphological dictionary mecab–ipadic–NEologd [14] while
excluding symbols and numbers. For the TF–IDF calculation,
we utilized the TfidfVectorizer under the default parameters inthe sklearn.feature_extraction.text module.

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Thereafter, we constructed three classification methods using
the CPC corpus, the YJQA corpus, and their combined corpus,
referred to as the description-based (D-based) method,
example-based (E-based) method, and description and example
combination-based (D+E-based) methods, respectively.

Evaluation Methods
We evaluate the accuracy of each method by calculating the
proportion of correct classifications.
The proportion of correct classifications for the D-based method
is calculated as follows. First, we find the categories with thehighest cosine similarity between the word vectors of the CPC
corpus and the manually labeled YJQA corpus (top 1-10). Next,
we calculate the proportion of correct categories from 1. Here,it is counted as a correct category if at least 1 of the 3(maximum) categories is included. Based on the highest cosine
similarity, the calculated percentage is referred to as the top 1
accuracy (Acc@1). Similarly, using the top 10 cosine
similarities, the top 10 accuracies (Acc@10) are calculated. The
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proportion of correct classifications is calculated using 5-foldcross-validation to evaluate the E-based method [15]. Using thecosine similarity between the training and validation data sets,
the proportion of correct classifications is the mean and medianof the rate, as in the above calculation. For the evaluation of theD+E-based method, the proportion of correct classifications is
calculated by employing the same evaluation method as for the
E-based method using both the CPC and YJQA corpora.

Results

and Acc@10 of the D-based method were approximately 10%
and 30%, respectively. Furthermore, for both the E-based andD+E-based methods, they were approximately 50% and 70%,
respectively. The E-based method is an optimized classification
method used to classify YJQA questions. However, it does notcover all CPC categories, whereas the D+E-based method covers
all CPC categories, and the rate of correct answers is not
significantly different from that of the E-based method.
Therefore, in this study, we interpret the results of theD+E-based method.

Evaluation Results
Table 2 shows the proportions of correct classifications
calculated using the above evaluation methods. The Acc@1
Table 2. Accuracy for each method.
D-baseda

E-basedb

D+E-basedc

Mean

0.1096

0.48910.4781

SDd

–e

0.017

0.018

Median–0.48350.4725Mean

0.2946

0.69600.7062

SDd

–

0.030

0.201

Median–0.70150.7106

Accuracy
Acc@1

Acc@10aD-based: description-based method.

bE-based: example-based method.

c

D+E-based: description and example combination-based method.

d

SD: unbiased sample standard deviation.

e

For the D-based method, there are missing values because 5-fold cross-validation is not utilized as described in the evaluation method section.

Classification Results
We present the classification results of the D+E-based method
for the target data to be classified. Table 3 lists the top frequency
categories. The top 10 categories accounted for 61.9% of thetotal. The category with the most frequent questions was“worrying about cancer with subjective symptoms” (1661

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448 categories classified by the D+E-based method, and the
distribution of the top 30 categories is shown in Figure 1. The
rate of change from the top 1 to the top 2 categories was thelargest at 57.7%. Moreover, the rate of change from the top 20
categories was 20% to 40%, after which it was approximately
10%. As a result, the frequency distribution has a long tail.

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Table 3. Results obtained using the description and example combination-based method (top 10 categories).
CPC category code

CPC category nameFrequency (%)

16.3.1.1.

Worrying about cancer with subjective symptoms

1661 (23.8)

16.2.1.1.

Matters related to cancer screening

702 (10)16.3.2.1.

Concerns regarding suspicion of cancer (other)494 (7.1)

12.2.4.1.

Anxiety due to lack of knowledge about cancer

419 (6)3.1.3.5.

The received treatment (choice), whether it is correct

255 (3.6)

3.2.2.2.

Worrying about the results and its trend

234 (3.3)

12.1.1.1.

Anxiety about the possibility of recurrence or metastasis

225 (3.2)

9.1.2.2.

Difficulty in asking questions or expressing concerns to the doctor

137 (2)12.3.2.3.

I can’t stop thinking about cancer

111 (1.6)

9.1.1.1.

Doctor's words and attitude

93 (1.3)

Figure 1. Classification using the D+E-based method (X-axis) and its frequency (Y-axis; top 30 categories). D+E: description and example
combination-based method.

Similarity of Distribution Between Manual
Classification and Our Method
We evaluated whether the frequency distribution of the proposed
method was close to that of the real method. Comparing theclassification results with the manual classification results (Table1), we found that 7 out of 10 categories with high frequency
were the same, and the first and the second category in bothcases were “worrying about cancer with subjective symptoms”
and “cancer screening.”
The frequency distribution of the CPC, including the
low-frequency part, was compared between the proposed andmanual methods. The top 30 categories' frequency distributionsin the D+E-based method were used for visual and numerical
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evaluation of all categories. Figure 2 shows the distribution of
the classification results using the D+E-based method andmanual classification. The distributions were similar. In addition,
we calculated the Jensen-Shannon divergence [16] for all
categories to measure the distance between these distributions.
Values closer to zero indicated higher degrees of similarity in
distribution.
The value of the Jensen-Shannon divergence for the distributionof the manual classification and D+E-based classification result
is 0.105, which shows that the 2 distributions are similar. Even
though the individual accuracy was low, the CPC distribution
obtained by the proposed method was closer to the correct one.

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Therefore, it is possible that the proposed method can be used
to conduct a large-scale survey of patient concerns

Kamba et alautomatically.

Figure 2. Distribution of the classification results using the D+E-based method and manual classification.

ExamplesThe 3 examples in this section show how the consistency ofactual questions and results was confirmed and how the side
effects of drugs and unmet needs were extracted.
Table 4 shows the questions estimated to have high cosine
similarity. Table 5 shows the questions classified into category
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signaling. In Table 5, we included the code and name ofcategories classified by our model, drug name, and side effects
that could be read from the text of the questions. Table 6 showssome of the questions and their categorization for the
low-frequency categories and “COVID” search to extract unmet
needs. In Table 6, we included the code and name of categories
classified by our model and unmet needs that could be readfrom the text of the questions.

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Table 4. Questions estimated to have high cosine similarity.
Questions (translated to English from Japanese)

Cosine similarity Code and name of categories

Please tell me what makes you susceptible to breast cancer!

0.714

15.1.1.5. I was told that I havecancer

0.206

12.2.5.1. Suspecting or worrying about another type of cancer

0.204

11.3.1.1. Current health condition

I had one of my breasts removed due to breast cancer. I did not have simultaneous reconstruction. 0.682
My breasts are small, to begin with, so when I was asked about simultaneous reconstruction, I
didn’t think much about it and told my doctor that I would think about it after the surgery. In my
40s, I was admitted to the hospital, but most people my age had simultaneous reconstruction and 0.264
expanders. I wondered if I had made the wrong choice. Since If I have to do it later, I'll have tohave one more surgery, I think it’s okay as is. I heard that it takes quite a few days to reconstruct. 0.255
It needs one year at the earliest. Moreover, I heard that nipple and areola surgeries are different.
That’s a long time. But I still think I want to have reconstruction. If you have reconstructed, if
you haven’t reconstructed, if you have reconstructed in another way, if it’s not covered by insurance, etc, please give me some advice! I’d like to hear about your experiences. It will be twomonths until my next visit to the hospital. I want to ask my doctor many questions, so if you
could tell me anything, I would be very happy. Please give me some advice. Moreover, it seems
that the implants and expanders for reconstruction have been discontinued because they are carcinogenic. I don’t think I will be able to have reconstruction for a while, but please advise me.

13.3.1.6. Matters related tobreast reconstruction

I am undergoing treatment for breast cancer, and my white blood cell count has dropped due to 0.657
side effects, so my immune system is not high. I don’t want to go to the birthday party at myparents-in-law’s because I’m worried that I might get infected with the coronavirus. My motherin-law and father-in-law know that I am undergoing treatment and my immunity is low, but they 0.515
don’t want to cancel the party because it’s their adorable grandchild’s birthday. It’s hard for me
to tell them. I don’t want my husband to go either, but he doesn’t seem to mind at all. Is there
any way to avoid attending the party?
0.417

11.1.2.3. Persistent side effects
of anticancer drugs (other)

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3.1.1.1. Uncertainty about
treatment options
3.1.3.5. The received treatment(choice), whether it is correct

11.1.1.8. Symptoms of side effects from anticancer drugs(other)15.2.16.1. Relationship withfamily (Other)

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Table 5. Questions that were classified into the categories of category 11.
Questions (translated to English from Japanese)a

CPCa category code andnameDrug nameSide effects

I am undergoing treatment for breast cancer, and my white blood cell count
11.1.2.3. Persistent side efhas dropped due to side effects, so my immune system is not high. I don’t want fects of anticancer drugs
to go to the birthday party at my parents-in-law’s because I’m worried that I (other)might get infected with the coronavirus. My mother-in-law and father-in-law
know that I am undergoing treatment and my immunity is low, but they don’t
want to cancel the party because it’s their adorable grandchild’s birthday. It’s
hard for me to tell them. I don’t want my husband to go either, but he doesn’t
seem to mind at all. Is there any way to avoid attending the party?

Anticancer drug forbreast cancer

Leukopenia

Can I improve the numbness caused by the side effects of anticancer drug
treatment? My sister is undergoing anticancer treatment for breast cancer, andshe is suffering from numbness in her hands and feet. Is there anything she
can do to relieve the numbness? Does she have to stop the anticancer treatment?

Breast cancer drug

Numbness

I am undergoing anticancer treatment, FECb treatment with infusions every 3 11.1.1.1. Hair loss due toanticancer drug treatment
weeks, breast cancer. I have completed four courses, and I am about to startanother one, and I have a question about hair loss. My hair still looks like ababy’s, so I can say that I am losing hair. Although I have heard that otherparts of my body, such as the eyelashes, eyebrows, shins, and lower hair, I am
not losing other than my hair. My doctor said that you lose when I asked mydoctor about it the second time. I’m worried that the medication might not beworking correctly. If you have any experience with this or know anything aboutit, please advise me.

FEC treatment

Hair loss

I would like to know about mouth ulcers during anticancer treatment. I have 11.1.1.6. Mucosal damage
breast cancer and will start anticancer treatment, but before that, I went to acaused by anticancer drugs
dentist and was told that I should have my teeth treated. She told me that I(stomatitis, etc)
would probably get many mouth ulcers from the anticancer treatment but that
I should just go and see her. She told me that I should go in. If it’s a common
mouth ulcer, I’m sure they can treat it with ointment, but I’m not sure if themouth ulcer will begin to heal before the anticancer drugs are finished? The
side effects of the anticancer medicines make it hard to go to the dentist, andthe thought of having to go stresses me out. If I can heal my mouth ulcers fasterby going to the dentist, I’ll do my best. However, if it doesn’t make much difference, I don’t want to push myself as much as possible because of the hairloss, fatigue, side effects, and other things. If I go to the hospital because ofmouth ulcer, will it heal faster? If you have any experience or know of anyone
who had mouth ulcers, please let me know.

Breast cancer anticancer drug

Stomatitis

My 66-year-old mother is undergoing anticancer treatment for the lung’s adenocarcinoma. She is taking Docetaxel plus Cyramza once every four weeks.
She had numbness after the second dose and reduced the dose for the third
dose, but the numbness keeps getting worse…She’s been taking the maximum
daily dose of Lyrica to reduce the numbness, but she says it’s not helping at
all. She can’t walk anymore, and it has become mentally painful for her, so
we are hoping that we can alleviate her numbness. Can you tell me anything
about how to deal with the numbness, herbal medicine, or anything else thatmight help reduce the numbness a bit? Thank you very much.

Docetaxel + Thyramza

Numbness

11.1.1.2. Nerve damage
such as numbness and discomfort caused by anticancer drugs

11.1.1.2. Nerve damage
such as numbness and discomfort caused by anticancer drugs

CPC: Cancer Program Classification.

b

FEC: fluorouracil, epirubicin, and cyclophosphamide.

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Table 6. Questions and their classification categories considered as unmet needs.
Questions (translated to English from Japanese)

CPCa category code andname

Unmet needs

My mother has breast cancer with bone metastasis. I heard that bone metastasis has a high risk 8.2.1.1. Traffic condiof fracture, so should I prevent her from driving a car in the future? Moreover, my 80-year-old tions are bad
grandmother is still driving. However, there are many accidents involving the elderly, and therisk of having an accident is probably higher than for younger people. If I assume the worstcase scenario, should I stop her from driving instead of saying, “It’s a pity to take away her
car?” If I ask her to quit driving, in what situation/venue should I tell her? Moreover, I have adriver’s license, but I’m a driver on paper only. Should I go back to school to drive for mymother and grandmother when we go out with the family? I don’t think I’ll be able to drive on
public roads since I have not driven for a long time…

Driving a car with a displaced bone cancer patient

Please tell me if I can sue for cancer misdiagnosis. Two years ago, I went to a hospital because 5.3.1.3. Should I get aa retest was required by mammography. Since there was something suspicious on the echo, I second opinion?
had cytology done on the spot. The cytology didn’t give me any results due to a bad specimen,
so I asked for histology. The doctor told me that I would have to stay overnight at anotherhospital for a mammotome biopsy, etc. I didn’t want to spend a lot of time figuring out what
was black and white, so I had a surgical biopsy, a definitive diagnosis that could be done at that
hospital. As a result, I was diagnosed with “breast adenopathy” and told to visit the hospital
regularly. But some of the results of the tissue examination were not convincing, so I had theexamination done at another hospital. The result was breast cancer…how could they remove
it from the definitive diagnosis…I would be horrified if they were convinced it was mammary
gland disease and discovered it too late. I want to sue the doctor who is still examining andtreating me as usual, but I heard that medical lawsuit are difficult. Is it possible to sue him? Do
I have a chance to win?

Lawsuits against misdiagnosis

I had breast cancer sparing surgery in early February and will start radiation treatment in April. 8.2.1.3. Frequent visits to Worried about corona inHowever, I am going through a tough time with corona right now, and I feel anxious about
the hospital are difficult fection due to hospitalgoing to the hospital every day. Is there anything else I can do except taking personal measures?
I had a breast cancer sparing surgery in February this year and was scheduled for radiation
11.1.3.6. Symptoms ofTreatment postponed due
therapy, but it has been postponed due to the coronavirus. It will still take some time for theradiation-related side ef- to coronavirus
situation to improve, but should I avoid starting radiation therapy at this time? I am on hormone fects (other)
therapy, but I am getting anxious about not undergoing radiation therapy.

a

My 88-year-old mother is in a special care facility and has a fever of 37.5. She has breast cancer, 11.2.1.5. Fever
so I don’t know if the fever is caused by breast cancer, corona, or a cold. What are the symptoms
of a fever caused by breast cancer? Do I need to see my family doctor? If it is not caused bybreast cancer, does the fact that I have a high fever in a special care facility mean that I havecontracted the virus from a staff member?

I can’t tell if it’s cancer
symptoms or corona
symptoms

About 18 years ago, my mother was diagnosed with breast cancer. She had an operation and12.1.1.1. Anxiety about
has been living a normal and healthy life since then. However, 2 years ago, she was told thatthe possibility of recurthe cancer had spread to her lungs. At present, she has difficulty breathing even when she moves rence or metastasis
a little, probably due to the accumulation of pleural effusion. When she was told that the cancer
had spread, the doctor did not give her a life expectancy, but when she looked it up on the internet, she found all sorts of information that made her feel uneasy. Can you tell me whether
she will live much longer or whether she may be able to live longer while coping with her illness?
I’m getting married soon, and I was planning to show her my wedding dress next year. However,with the corona epidemic, that plan is now undecided. I want to show her my wedding dress atleast. I’m not sure if this is practically possible.

I want my mother to seeme in my wedding dress,
but she has been diagnosed with cancer

5.3.1.6. Matters related
When I distrusted the female surgeon at the [omitted]b Hospital and applied for a second
opinion (a letter of introduction was required), I was pressured to go to the hospital for a second to second opinions (othopinion. The doctor there is a surgeon famous for his breast-conservation therapy, but he didn’t er)
listen to me very carefully and told me that he agreed with Dr. [omitted] (the doctor in charge
at [omitted] Hospital) and that I should tell her that he agreed with her because doctors have adifficult relationship with each other. Is there such a thing? The book on breast cancer publishedby the [omitted] Hospital, famous for cancer treatment, claims it to be the “standard treatment,”
even though the treatment policy is different. Is there anyone who was notified that they hadcancer and went for a second opinion and then were offered a different treatment plan? Do
doctors always protect their doctors? I was amazed at the lecturers’ pride in the national university hospital (even though they are quacks).

Not fulfilling the role ofa second opinion

CPC: Cancer Program Classification.

b

We blinded the proper noun because it is not relevant to extract the unmet needs.

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Kamba et al

Discussion

method, of which 100 (21.3%) cases were randomly sampled,and 15 (3.2%) had specific drug names.

Consistency of the Actual Questions and Results

Potential Application for Unmet Needs

Here we discuss the consistency of the actual questions andresults with high and low cosine similarity, respectively. In
Table 4, it is unclear whether a cancer patient asked the firstquestion, but it appears to express concern about the possibilityof developing cancer. The second question was about breastreconstruction, and the third was a concern about coronavirus(COVID-19).

Patients’ unmet needs are becoming a major societal issue. In
particular, the unmet needs of those who should answer have
not yet been sufficiently addressed. Except for a few fee-based
QA sites [17,18], QA sites are generally answered bynonexperts, but some questions should be answered byphysicians.

We also discuss questions that could not be correctly classified
in Table 4. The reason for the inability to classify the questions
with the highest cosine similarity correctly can be the use of
the cosine similarity between the word vectors in the bag ofwords, and the context could not be taken into account. Morespecifically, since the question included the word “constitution,”
it was considered to be classified in the category that included
the word “constitution.” Similarly, the top 2 worries about breastreconstruction could be classified in the CPC category, which
includes the phrase “breast reconstruction.” The top 3 problems
are related to COVID-19, which is not included in the current
CPC category, and therefore must be newly defined.
As for the results with the cosine similarity from the lowest to
third-lowest, the question was a request for Japanese translationfrom English and was not in itself a question about breast cancer.
This is because questions including the phrase “breast cancer”
were also extracted when searching for “breast cancer,” and thedata acquisition method must be improved in the future.

Clinical Application
In the previous section, we noted that this research was effective
for statistical surveys. In addition, we believe that there are otherpossible applications. In particular, we will examine theextraction of adverse drug events (signal detection) and theextraction of unmet needs.

Potential Application to Side Effect Signaling
Extracting side effects from the submitted questions would bevery beneficial for pharmaceutical companies and patients
because it would allow them to collect significant information
on drug safety. Specifically, since some questions classified
under the overarching category of “symptoms, side effects, andsequelae” (category 11) of the CPC are considered to contain
information on side effects, we can extract such information by
applying intrinsic expression extraction to the question text.
In Table 5, the first question contained information about thedrop in white blood cells, and the second contained information
about numbness in the hands and feet; however, we could not
identify the drug that caused the side effect because there was
no information about the drug, and the third contained
information about the side effects of hair loss due to fluorouracil,
epirubicin, and cyclophosphamide (FEC) treatment, a type ofchemotherapy. However, the third question simply indicatedthat FEC treatment caused a side effect called hair loss. Thus,
although side effects can be extracted, the granularity of the
drug information may be insufficient. Of the 6993 cases, 470(6.7%) were classified under category 11 using the D+E-based
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Unmet needs are needs that are not addressed due to a lack ofservices or resources or that have never existed before. The
former may be found by discussing the high-frequency
categories with medical workers, which may help identify needs
that have been insufficiently addressed in the past, althoughmany patients complain about them. The latter can be extracted
by searching for low-frequency categories or words that havebecome popular in recent years (eg, “COVID”).
In Table 6, the first example is an unmet need (car driving) of
a cancer patient with bone metastasis, the second is a
misdiagnosis lawsuit, and the third is an unmet need related to
COVID-19. In this study, unmet needs were extracted by reading
the questionnaire; however, constructing an automaticclassification model for unmet needs is a future task.

Limitations and Future Work
Since we used the Japanese text of questions found by the searchphrase “breast cancer” as the training data of the method in thisstudy, the method may not apply to other cancer types and othercountries. However, our method can be expanded to different
cancer types and countries in cases where problem data are
available. Here, the cancer problem categories specific for othercountries are needed because they were defined for Japanesepeople in this study. When expanding our method to other cancer
types and countries, future work will have to focus on
reproducibility. Therefore, it is necessary to reconstruct thetraining data from the questions found by searching for eachcancer word to apply the method to other cancer types.
In addition, COVID-19 infections in Japan appeared in February
2020, and patients with cancer might experience
COVID-19-related problems. Therefore, it is possible that thecurrent CPC categories may not be able to ensure properclassification. Thus, it is necessary to define new problem
classification categories for patients with cancer after February
2020. In addition, since new topics, not limited to COVID-19,
are always likely to occur, it is necessary to construct a modelthat could extract such uncommon topics.
The target of this study was question texts posted on QA
services, and it may not be possible to classify other texts
correctly. The fact that the accuracy of the D-based method was
extremely poor among the 3 methods may be due to thedifference between the questionnaire text used in the CPC andthe text posted on the YJQA. We also found that cancer patients’problems are not limited to questions posted on the QA website
but also Twitter and blogs. It is necessary to broaden the training
data of the classification method for these texts to classify theworries of cancer patients. In addition, there are many posts in
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which the content is unrelated to worries or contains too manyemojis. Therefore, it is necessary to build a model to determine
whether a post contains worries. Subsequently, 2 schemes areneeded to classify the blogs containing worries into CPC
categories.

Conclusions

Kamba et al
approximately 70%. Although classification alone does not
solve patients’ problems, a comprehensive understanding of thetype and number of problems can help prioritize services tosolve problems from the patients’ point of view. We would liketo examine the services that could be provided in the future
based on this information.

This paper proposed a method to classify questions submitted
to the YJQA into the CPC with a correct answer rate of

Acknowledgments
This study was supported by Health Labour and Welfare Sciences Research Grants (H30-Gantaisaku-Ippan-009) from the Ministryof Health, Labour and Welfare, Japan.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Supplementary table containing all the Cancer Problem Clarification (CPC) categories obtained using the D+E-based method.
[XLSX File (Microsoft Excel File), 28 KB-Multimedia Appendix 1]

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Abbreviations
CPC: cancer problem classification
FEC: fluorouracil, epirubicin, cyclophosphamide
QA: questions and answers
TF–IDF: term frequency-inverse document frequency
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Kamba et al

YJQA: Yahoo! Japan questions and answers

Edited by G Eysenbach; submitted 14.07.21; peer-reviewed by S Matsuda, MS Hosseini; comments to author 02.08.21; revised version
received 25.09.21; accepted 04.10.21; published 28.10.21
Please cite as:
Kamba M, Manabe M, Wakamiya S, Yada S, Aramaki E, Odani S, Miyashiro I
Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-BasedApproach
JMIR Cancer 2021;7(4):e32005
URL: https://cancer.jmir.org/2021/4/e32005
doi: 10.2196/32005
PMID:©Masaru Kamba, Masae Manabe, Shoko Wakamiya, Shuntaro Yada, Eiji Aramaki, Satomi Odani, Isao Miyashiro. Originallypublished in JMIR Cancer (https://cancer.jmir.org), 28.10.2021. 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 Cancer, is properly cited. The complete
bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license
information must be included.

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