JMIR FORMATIVE RESEARCH

Han et al

Original Paper

Extraction and Quantification of Words Representing Degrees of
Diseases: Combining the Fuzzy C-Means Method and GaussianMembershipFeng Han1, MSc; ZiHeng Zhang1, MSc; Hongjian Zhang2, MSc; Jun Nakaya3, MD; Kohsuke Kudo4, MD; KatsuhikoOgasawara2, MBA, PhD
1

Graduate School of Medicine, Hokkaido University, Sapporo, Japan

2

Graduate School of Health Sciences, Medical Management and Informatics, Hokkaido University, Sapporo, Japan

3

Division of Advanced Diagnostic Imaging Development, Graduate School of Medicine, Hokkaido University, Sapporo, Japan

4

Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan

Corresponding Author:
Katsuhiko Ogasawara, MBA, PhD
Graduate School of Health Sciences
Medical Management and Informatics
Hokkaido University
N12 W5
Sapporo, 060-0812
Japan
Phone: 81 011 706 3409
Email: oga@hs.hokudai.ac.jp

Abstract
Background: Due to the development of medical data, a large amount of clinical data has been generated. These unstructured
data contain substantial information. Extracting useful knowledge from this data and making scientific decisions for diagnosing
and treating diseases have become increasingly necessary. Unstructured data, such as in the Marketplace for Medical Informationin Intensive Care III (MIMIC-III) data set, contain several ambiguous words that demonstrate the subjectivity of doctors, suchas descriptions of patient symptoms. These data could be used to further improve the accuracy of medical diagnostic systemassessments. To the best of our knowledge, there is currently no method for extracting subjective words that express the extent
of these symptoms (hereinafter, “degree words”).
Objective: Therefore, we propose using the fuzzy c-means (FCM) method and Gaussian membership to quantify the degree
words in the clinical medical data set MIMIC-III.
Methods: First, we preprocessed the 381,091 radiology reports collected in MIMIC-III, and then we used the FCM method to
extract degree words from unstructured text. Thereafter, we used the Gaussian membership method to quantify the extracted
degree words, which transform the fuzzy words extracted from the medical text into computer-recognizable numbers.
Results: The results showed that the digitization of ambiguous words in medical texts is feasible. The words representing each
degree of each disease had a range of corresponding values. Examples of membership medians were 2.971 (atelectasis), 3.121(pneumonia), 2.899 (pneumothorax), 3.051 (pulmonary edema), and 2.435 (pulmonary embolus). Additionally, all extracted
words contained the same subjective words (low, high, etc), which allows for an objective evaluation method. Furthermore, wewill verify the specific impact of the quantification results of ambiguous words such as symptom words and degree words on theuse of medical texts in subsequent studies. These same ambiguous words may be used as a new set of feature values to represent
the disorders.
Conclusions: This study proposes an innovative method for handling subjective words. We used the FCM method to extract
the subjective degree words in the English-interpreted report of the MIMIC-III and then used the Gaussian functions to quantify
the subjective degree words. In this method, words containing subjectivity in unstructured texts can be automatically processed
and transformed into numerical ranges by digital processing. It was concluded that the digitization of ambiguous words in medical
texts is feasible.

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Han et al(JMIR Form Res 2022;6(11):e38677) doi: 10.2196/38677
KEYWORDS
medical text; fuzzy c-means; cluster; algorithm; machine learning; word quantification; fuzzification; Gauss; radiology; medical
report; documentation; text mining; data mining; extraction; unstructured; free text; quantification; fuzzy; diagnosis; diagnostic;
EHR; support system

Introduction
Owing to the development of medical data, several electronic
medical reports such as clinical records have been created, which
provide a large amount of clinical data to the medical
professional [1]. They have been shown to have capabilities
such as contributing to health care knowledge discovery
processes, for example, disease phenotyping and diagnosis [2,3],
identification of new associations [4], development of disease
surveillance systems [5], and health care monitoring systems[6]. Electronic medical reports are categorized into structured
and free-text formats [7]. Where unstructured clinical notes
contain rich subjective information [8-10]. A radiology report
records a patient’s condition created by a health care
professional, such as a doctor, and contains medical evaluation
information [11]. Although, with the advent of natural language
processing (a branch of artificial intelligence applicable tounstructured textual data), clinical text mining is increasingly
used in various health domains, there is still relatively little
text-based machine learning research available compared to themore common structured numerical data-based analysis [12].
The traditional method involves manual extraction of symptoms.
Hyun et al [13] extracted symptoms by manually defining a
dictionary of symptoms. Jagannatha and Yu [14,15] and Fodeh
et al [16] manually labeled and extracted symptoms. Matheny
et al [17] set rules to extract symptoms. Chen and Sarkar [7]and Tamang et al [18] used a combination of several of thesemethods to extract symptoms. The disadvantage of thesemethods is that they require considerable time and labor costs,
and are error-prone [11].
Reátegui and Ratté [11] and Wu et al [19] extracted symptoms
using the entity naming and extraction method. It improves the
disadvantages of the manual extraction method by using acomputer to automatically extract named entities from a large
amount of text to extract symptoms, but unstructured data needs
to be associated with entities to extract symptoms. Therefore,
there is a risk of errors in modeling and naming entity
relationships, which can affect the results.
De Silva et al [20] used the machine learning least absoluteshrinkage and selection operator (LASSO) or ridge
regularization method to extract symptoms from unstructured
text (Marketplace for Medical Information in Intensive Care III[MIMIC-III]) and to predict mortality in patients with diabetes.
Since LASSO uses a machine learning approach, it does not
require association with entities and saves manual time andlabor cost.
Notably, these methods were limited to the extraction ofsymptoms [11,14-17,21,22] and did not extract words that
express the degrees of these symptoms (hereinafter, “degree
words”). These degree words also contain information about
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doctors and patients if extracted from unstructured data that can
provide a basis for judgment in medical diagnostic systems, andtherefore, it is necessary to process these data to further improve
the judgment accuracy of the medical diagnostic system [22].
However, unlike structured data, these degree terms include thesubjectivity of doctors. They do not have a unified standard andcannot be processed directly by using a computer [21].
In 1965, Zadeh [23] of the University of California, Berkeley
published the first paper on fuzzy theory, submitting for the
first time the concept of fuzzy sets from the perspective of set
theory to describe things with fuzziness and to describe thingsin everyday life by fuzzy logical reasoning, similar to the humanthinking patterns and the probability theory proposed by Zadeh[24], describing the difference between randomness andprobability, which is considered as the second milestone in thedevelopment of fuzzy mathematics. The emergence of fuzzy
theory has provided a solid theoretical foundation and effective
tools for the wide application of fuzzy mathematics in patternrecognition and other fields.
The concept of fuzzy theory emphasizes the use of fuzzy logic
to describe things in real life and to make up for the
shortcomings of classical logic (binary logic), which cannot
describe things with unclear boundaries. Human natural
language is vague in its presentation, and it is difficult to fully
describe real-world problems using the dichotomy of “right orwrong” and “good or bad.” Therefore, fuzzy theory uses thedefinition of a fuzzy set to define and quantify the membershipfunction (membership rank) of the degree to which an event
belongs to this set, and solves different problems by quantifying
the membership rank (membership value) [25].
Fuzzy c-means (FCM) is an unsupervised soft computing
technology developed by Dunn [26] in 1973 and improved by
Bezdek et al [27] in 1981. Unlike the hard clustering method,
the soft clustering method uses fuzzy sets [23], which can solve
the problem of text ambiguity better. In fuzzy sets, membershipindicates the matching degree between the element and the set,with membership values ranging from 0 to 1. Furthermore, theconcept of membership is extended in the FCM method, wherein
the membership matrix represents the membership values ofthe elements in multiple clusters. FCM is one of the mostcommonly used methods to solve fuzzy problems. Comparedwith other clustering methods, it is more flexible and canrepresent the degree of data affiliation more appropriately [28].
The proposed method has two main advantages. First, it isunsupervised; therefore, labeled data is not required during
training. Second, it can resolve subjective word ambiguity infuzzy sets and medical texts such as a description of symptoms.
Therefore, we propose using the FCM and Gauss membershipmethods to quantify the subjective degree words in the
English-interpreted report of the MIMIC-III data set.
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Methods
Overview
The overall structure of the proposed method is shown in Figure
1. It contains five parts: preprocessing, feature extraction,clustering, digitization, and visualization. All the calculation
methods used in this experiment were implemented in Python.

Han et alFirst, the raw data are preprocessed. Next, a skip-gram is used
to extract word features and convert them into
computer-processable data. Thereafter, the processed data areclustered. Subsequently, clustered data are digitized. Finally,
the results of clustering and digitization are visualized to makethe data structure easier to understand.

Figure 1. Overall structure of the proposed method.

Data Set
The data set used in this study was an interpretation report of522,279 physicians from the MIMIC-III data set [29]. Five lungdiseases (atelectasis, pneumonia, pneumothorax, pulmonary

edema, and pulmonary embolus) were selected from Normalized
Clinical Knowledge (NCK) [30] as keywords for the searched
MIMIC-III data set. The search results are presented in Table
1. The number of texts used in the experiment was 381,091.

Table 1. Search results for 5 diseases.
Keywords

Texts (N=381,091), nAtelectasis

119,765

Pneumonia

72,553Pneumothorax

138,785

Pulmonary edema

47,364

Pulmonary embolus

2624

Preprocessing
The input data set was preprocessed using normalization andPython code using the following tasks:
•
•
•

•
•
•

Text classification: Filter text according to databasekeywords from NCK and classifications of various diseases
Merge similar text: Merge all data for the same illness into
one file to create a corpus of illnesses
Remove special characters: All special characters(punctuation marks, question marks, exclamation marks,etc) are removed from the text and replaced with spaces
Case exchange: Change all letters to lowercase to reduce
vocabulary repetition
Delete stop words: Delete stop words with no special
meaning (eg, am, name)Tag words: To extract words, we used the Natural Language
Toolkit to tag words.

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Word extraction: Degree words are primarily adjectives
and adverbs, and illnesses are primarily nouns; therefore,
only adjectives, adverbs, and nouns should be extracted.

Feature Extraction
Feature extraction must be used to convert natural language
into computer-processable numerical data. We used word2vec
skip-gram [31] to extract features from the collected data. For
the word vector to reflect the contextual relationships, the data
used in the word vector training were not the extracted words
but the text with the stop words removed. The extracted words
were used for cluster analysis. Furthermore, to exclude irrelevant
words, we removed words with fewer than 10 occurrences. The
dimension of the word vector was set to 100 [32]. This implies
that each word was represented as a 1 × 100 vector. Text
consisting of N words was represented as a matrix size of N ×100. These word vectors can contain the positional relationshipand structural information of each word in the text.

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Fuzzy C-Means
We used the FCM method to cluster the features. This method
allows symptoms of the same disease in different texts to begrouped into the same category. Each element has a membershipvalue for each category. The degree of membership depends onthe distance from the element to the center of the cluster, andthey are inversely proportional [33]. In this study, the numberof clusters for each disease was set to 10. The fuzzy index msetting was set to 2 [34]. The loop was set to 50 times.

Fuzzification
To import the trained model as a basis for evaluation into a
medical diagnostic system, digitization must be used to convert
the FCM results into a numerical range. In this experiment, theEuclidean distance from each word to its center was used as areference to digitize the word. The formula for calculating the
distance is shown in equation 1.

The more popular membership functions are the triangularmembership function and the Gaussian membership function,and since the center of the triangular affiliation function is too
steep, this experiment uses the Gaussian membership function
for the numerical range conversion. This membership function
is given by equation 2. The center of each Gaussian membershipfunction can be determined based on these distances, where x1k

Han et alis the kth coordinate value of the center and x2k is the kth
coordinate value of the element.

Where σ is the width parameter to control radial range offunction. For clustering problems, if the clustering structure in
the feature space of sample points is compact, the smaller σ canensure the effect of the sample points clustering. If the clustering
structure is dispersed, the larger σ can help to obtain the explicit
membership function distribution. We set the σ value to 0.4.
It is necessary to ensure that the membership function takes
values in the range of 0-1 in the fuzzy set [35]. Therefore, we
set the boundary c to 0.1.

Principal Component Analysis and Data Visualization
In addition, it is necessary to visualize the results of the twoparts (ie, clustering and digitization). To visualize the results
after clustering, principal component analysis (PCA) must be
used to reduce the dimensions of the data. To make the data
easier to understand, we used PCA [36] to projecthigh-dimensional data into a low-dimensional space. PCA canreduce the dimensions of data while retaining key informationand increasing readability. The numerical results will betransformed into easy-to-understand 2D plots. Figure 2 is thevisualization result of the disease atelectasis.

Figure 2. Visualization result of the disease atelectasis.

Results
This section introduces the results of this study. First, the resultsof the clustering process, data dimension reduction, andvisualization are described. Thereafter, the results of the
proposed fuzzy method are presented.

Fuzzy C-Means
Textbox 1 lists the results of clustering different diseases. The[Atelectasis] cluster contained degree words such as “quickly”
and “with small,” and symptom words such as “obstruction.”
The [Pneumonia] cluster contained degree words such as “small”

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RenderXand “mid,” site words such as “pulmonary,” and symptom words
such as “effusions.” The [Pneumothorax] cluster contained
symptom words such as “pain” and “fever,” and degree words
such as “expansion.” The [Pulmonary edema] cluster contained
symptom words such as “nausea” and “neutropenic,” and degree
words such as “copious.” The [Pulmonary embolus] cluster
contained degree words such as “expansion.” Different diseases
will produce different degree words and symptoms. These
degree words and symptoms were used to judge diseases. To
facilitate understanding of the variability of degree words
between diseases, we have chosen the same words for
explanation below.

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Han et al

Textbox 1. Clustering results. The italics indicate extracted degree words that are processed through fuzzification afterward.
Atelectasis“quickly,” “over penetrated,” “stenosis,” “small,” “qualified,” “midthigh,” “unlike,” “hepatic,” “wet massive,” “hypodensities,” “effusion with,”“high,” “obstruction,” “ultra-fast,” “tine”...
Pneumonia“pleural,” “lung,” “high,” “effusion,” “pulmonary,” “tube,” “lobe,” “lower,” “contrast,” “atelectasis,” “unchanged,” “small,” “upper,” “bilateral,”“normal,” “edema,” “effusions,” “opacity,” “within,” “consolidation,” “stable,” “mild,” “increased,” “consistent,” “unremarkable,” “clear,” “moderate,”“enlarged,” “large,” “low,” “opacification,” “well,” “mid,” “slightly,” “improved,” “significant,” “improvement,” “severe,” “minimal,” “slight,”“decreased,” “less”...
Pneumothorax“pain,” “fever,” “worsening,” “cough,” “increasing,” “expansion,” “exacerbation,” “vomiting,” “small,” “sputum,” “febrile,” “high,” “syncope,”“decreasing,” “overdose”...
Pulmonary edema“swelling,” “high,” “nausea,” “diarrhea,” “rising,” “neutropenic,” “prematurely,” “copious,” “malaise,” “instability,” “small”...
Pulmonary embolus“thin,” “reduced,” “small,” “inflated,” “expansion,” “high,” “anomaly,” “hyperostosis,” “overinflation,” “heights”...

Fuzzification
Figure 3 shows a membership diagram of the degree word
“low.” The x-axis represents the distance from the center. The
y-axis represents the membership value. The median Gaussian
membership is the distance from this element to the cluster’scenter. The greater the distance from the center point, the lower
the degree of membership. The maximum value at the center
point is 1.

Table 2 shows the central values for membership of the sameword for the five illnesses. The differences between conditions
can be determined by comparing the same vocabulary for
different conditions. By comparing the words of each degree,
we can identify the presenting characteristics of each symptom
and create a table of the possible symptoms of the patient bycomparing them to the patient’s presenting characteristics.

Figure 3. Membership chart of the degree word low.

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Table 2. Medians of the same word for 5 illnesses.
Low, medianHigh, median

Small, medianLarge, median

Atelectasis2.97132.71612.45472.4047

Pneumonia

3.12172.93832.43542.4190Pneumothorax

2.89982.82402.39562.3285

Pulmonary edema

3.05182.98802.45392.4003Pulmonary embolus

2.43571.79082.64192.5570Discussion
Principal Findings
Our proposed method is capable of identifying and extracting
degree words from unstructured text (Textbox 1) and transformthem into computer-recognizable membership values (Table2). The results in Textbox 1 show that all extracted words
contain the same subjective words (low, high, etc), which allows
for an objective evaluation method. These same ambiguous
words can be used as a new set of feature values to represent
the disorders. That is, the same subjective words can be used
to determine a disease. Thereafter, we quantified the extracted
subjective words using the Gaussian membership method totransform the features contained in the ambiguous words into
numerical features.
For the fuzzification section, we used the Gaussian fuzzy
function to transform the fuzzy words extracted from the medical
text into numbers. The results in Figure 3 and Table 2 show that
it is feasible to transform the features contained in the subjective
words into digital features. The digitization of ambiguous words(symptomatology and degree words) can improve the use of
medical texts, which is crucial for improving the accuracy andinterpretability of medical diagnostic systems. Furthermore, wewill verify the specific impact of the quantification results for
ambiguous words such as symptom words and degree words
on the use of medical texts in subsequent studies to improve
the accuracy and interpretability of medical diagnosis systems.

Comparison With Other Methods
Fodeh et al [16] used a random forest classifier to identify
clinical notes with pain assessment information. They extracted
subjective words describing pain intensity such as mild,moderate, and severe from the EHR and used them to help
determine whether the text described pain. The results showed
higher accuracy for the method using subjective words. The
conclusion that subjective words can be used to further improvethe medical diagnostic system is the same as in this study.
Unlike this study, they only classified the text by subjective
words and did not further use subjective words as a basis for a
new condition determination. This study translates the subjective
words into features of the disorder by fuzzification.
Matheny et al [17] pointed out the importance of symptoms andproposed a rule-based approach to extract them. This method
is compared to this study, which requires using detailed rules,
the creation of which is limited to the medical personnel.
Manually writing rules requires considerable time and may not
be exhaustive. The unsupervised method in this study requires
no manual processing and no human involvement, reducing
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time consumption and extraction omissions. Additionally, while
they note the importance of symptoms for the disorder, they do
not extract and analyze the subjective words.
The method that uses a dictionary to extract symptoms also
requires considerable time to construct and is not general [37].
For different tasks, rules need to be adapted. Additionally, therule formulation process involves deleting and adding rules anddiscussing the rule coverage. Therefore, substantial time costis required. Whereas, the FCM method is not limited to a
specific scope and has excellent generality. The rules are
summarized and extracted from the text, so they are more
broadly applicable.
De Silva et al [20] used the LASSO method to predict mortality
for patients with diabetes in MIMIC-III. The prediction results
suggest that the clinical text provides a resource for future
optimization and personalization of diabetes care. Additionally,
the LASSO method has strong generalizability. However, alarge amount of data is required to obtain more accurate results,
but currently there are far less data with fine-grained labels thanunlabeled data in the medical field. This method is equallyeffective for unlabeled data since it is an unsupervised method,
but the impact on the accuracy of the results needs further
discussion.
Reátegui and Ratté [11] used a named entity extraction system
to extract symptoms. It extracts symptoms by automaticallyextracting named entities from large amounts of text using acomputer, but it can create errors in modeling and named entity
relationships that can affect the results. As the methods in thisstudy do not need to be bound to named entities, these errors
do not occur.
Wu et al [19] developed a deep neural network (DNN) to
generate word vectors from a large, unlabeled, Chinese corpus
by unsupervised learning. The results show that the DNN-based
approach can capture grammatical features by word vectors andachieves higher performance compared with the traditional
conditional random fields approach. The time spent will be
substantially reduced by unsupervised learning. It is noted thatfurther performance improvement will be achieved if
high-dimensional discrete features are used. However, it isdifficult to use DNNs as a basis for diagnostic systems becauseof their black box effect. However, this study is able to give thebasis (affiliation) of the results while outputting the results
through the affiliation function. Therefore, it can be used as areference for medical practitioners.

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Limitations and Further Study
This study has some limitations. The first is data. There wereabbreviations and misspellings in the data, which were notextracted. The study has confirmed the presence of a large
number of abbreviations in unstructured texts that containequally large amounts of information [17]. We will try to extract
and analyze the presence of abbreviated words in the data in a
subsequent study. Additionally, the word2vec method we used
has the limitation that the vector representation does not change
when the words are used in different contexts. Due to thedifferent clinical contexts of diseases, a comparative study using
context-sensitive embedding algorithms is also needed. Finally,
the results for each word calculated in this experiment need tobe verified by the medical diagnostic system, and the method
we propose will be verified in future studies.
This study is mainly about the training of the model anddiscusses whether it is possible to quantify the subjective word
and use it as an indicator to determine the disease. In this study,
we used data from different conditions to quantify the subjective
words contained in them separately. Concerning future research,
we envision using the quantified data to process hospital-specificdata to give accuracy rates. For clinical application, we will
perform disease prediction based on the subjective words in thedata to get a list of possible diseases of the patient and give theHan et alcorrelation rate based on the fuzzy graph (Figure 3) generated
in this study. Thus, it will assist physicians in diagnosis.
Additionally, this method can solve the black box problem.

Conclusion
This study proposes an innovative method for handling
subjective words. We used the FCM method to extract the
subjective degree words in the English-interpreted report of
MIMIC-III and then used the Gaussian functions to quantify
the subjective degree words. In this method, words containing
subjectivity in unstructured text can be automatically processed
and transformed into numerical ranges by digital processing.
The results show that membership medians of “low” were 2.971(atelectasis), 3.121 (pneumonia), 2.899 (pneumothorax), 3.051(pulmonary edema), and 2.435 (pulmonary embolus). This
proves that the digitization of ambiguous words in medical texts
is feasible. Our results proved the feasibility of using subjective
words in medical texts. This makes it possible for medical
researchers to further improve the use of electronic medical
texts, thus affecting the accuracy and interpretability of the
medical diagnostic system. In future research, we will consider
how to extract abbreviations from abbreviation dictionaries andtexts, as well as further validate the impact on the accuracy andinterpretability of the medical diagnostic system.

Conflicts of Interest
None declared.

References
1.

2.

3.

4.

5.

6.

7.
8.
9.
10.

Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic
health record (EHR) analysis. IEEE J Biomed Health Inform 2018 Sep;22(5):1589-1604 [FREE Full text] [doi:10.1109/JBHI.2017.2767063] [Medline: 29989977]
Wu C, Kuo C, Su C, Wang S, Dai H. Using text mining to extract depressive symptoms and to validate the diagnosis ofmajor depressive disorder from electronic health records. J Affect Disord 2020 Jan 01;260:617-623. [doi:10.1016/j.jad.2019.09.044] [Medline: 31541973]
Sung S, Lin C, Hu Y. EMR-based phenotyping of ischemic stroke using supervised machine learning and text mining
techniques. IEEE J Biomed Health Inform 2020 Oct;24(10):2922-2931. [doi: 10.1109/JBHI.2020.2976931] [Medline:
32142458]
Sijbers A, Schoemaker R, Nauta A, Alkema W. Revealing new leads for the impact of galacto-oligosaccharides on gut
commensals and gut health benefits through text mining. Benef Microbes 2020 May 11;11(3):283-302. [doi:10.3920/BM2019.0105] [Medline: 32299221]
Ciofi Degli Atti ML, Pecoraro F, Piga S, Luzi D, Raponi M. Developing a surgical site infection surveillance system basedon hospital unstructured clinical notes and text mining. Surg Infect (Larchmt) 2020 Oct;21(8):716-721. [doi:10.1089/sur.2019.238] [Medline: 32105569]
Ali F, El-Sappagh S, Islam SR, Kwak D, Ali A, Imran M, et al. A smart healthcare monitoring system for heart disease
prediction based on ensemble deep learning and feature fusion. Inf Fusion 2020 Nov;63:208-222. [doi:10.1016/j.inffus.2020.06.008]
Chen E, Sarkar IN. Mining the electronic health record for disease knowledge. Methods Mol Biol 2014;1159:269-286.
[doi: 10.1007/978-1-4939-0709-0_15] [Medline: 24788272]
Hashir M, Sawhney R. Towards unstructured mortality prediction with free-text clinical notes. J Biomed Inform 2020
Aug;108:103489 [FREE Full text] [doi: 10.1016/j.jbi.2020.103489] [Medline: 32592755]
Banerjee I, Madhavan S, Goldman RE, Rubin DL. Intelligent word embeddings of free-text radiology reports. AMIA Annu
Symp Proc 2017;2017:411-420 [FREE Full text] [Medline: 29854105]
Korach Z, Yang J, Rossetti SC, Cato KD, Kang MJ, Knaplund C, et al. Mining clinical phrases from nursing notes todiscover risk factors of patient deterioration. Int J Med Inform 2020 Mar;135:104053 [FREE Full text] [doi:10.1016/j.ijmedinf.2019.104053] [Medline: 31884312]

https://formative.jmir.org/2022/11/e38677

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 11 | e38677 | p. 7
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
11.
12.
13.

14.
15.
16.

17.

18.

19.
20.

21.

22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.

33.

34.

35.

Reátegui R, Ratté S. Comparison of MetaMap and cTAKES for entity extraction in clinical notes. BMC Med Inform Decis
Mak 2018 Sep 14;18(Suppl 3):74 [FREE Full text] [doi: 10.1186/s12911-018-0654-2] [Medline: 30255810]
Demoulin NTM, Coussement K. Acceptance of text-mining systems: the signaling role of information quality. Inf Manage
2020 Jan;57(1):103120. [doi: 10.1016/j.im.2018.10.006]
Hyun S, Johnson SB, Bakken S. Exploring the ability of natural language processing to extract data from nursing narratives.
Comput Inform Nurs 2009;27(4):215-23; quiz 224 [FREE Full text] [doi: 10.1097/NCN.0b013e3181a91b58] [Medline:
19574746]
Jagannatha A, Yu H. Structured prediction models for RNN based sequence labeling in clinical text. Proc Conf Empir
Methods Nat Lang Process 2016 Nov;2016:856-865 [FREE Full text] [doi: 10.18653/v1/d16-1082] [Medline: 28004040]
Jagannatha A, Yu H. Bidirectional RNN for medical event detection in electronic health records. Proc Conf 2016
Jun;2016:473-482 [FREE Full text] [doi: 10.18653/v1/n16-1056] [Medline: 27885364]
Fodeh S, Finch D, Bouayad L, Luther SL, Ling H, Kerns RD, et al. Classifying clinical notes with pain assessment using
machine learning. Med Biol Eng Comput 2018 Jul;56(7):1285-1292 [FREE Full text] [doi: 10.1007/s11517-017-1772-1][Medline: 29280092]
Matheny ME, Fitzhenry F, Speroff T, Green JK, Griffith ML, Vasilevskis EE, et al. Detection of infectious symptoms from
VA emergency department and primary care clinical documentation. Int J Med Inform 2012 Mar;81(3):143-156. [doi:10.1016/j.ijmedinf.2011.11.005] [Medline: 22244191]
Tamang S, Patel MI, Blayney DW, Kuznetsov J, Finlayson SG, Vetteth Y, et al. Detecting unplanned care from clinician
notes in electronic health records. J Oncol Pract 2015 May;11(3):e313-e319 [FREE Full text] [doi: 10.1200/JOP.2014.002741][Medline: 25980019]
Wu Y, Jiang M, Lei J, Xu H. Named entity recognition in Chinese clinical text using deep neural network. Stud HealthTechnol Inform 2015;216:624-628 [FREE Full text] [Medline: 26262126]
De Silva K, Mathews N, Teede H, Forbes A, Jönsson D, Demmer RT, et al. Clinical notes as prognostic markers of mortality
associated with diabetes mellitus following critical care: a retrospective cohort analysis using machine learning andunstructured big data. Comput Biol Med 2021 May;132:104305. [doi: 10.1016/j.compbiomed.2021.104305] [Medline:
33705995]
Chen L, Song L, Shao Y, Li D, Ding K. Using natural language processing to extract clinically useful information from
Chinese electronic medical records. Int J Med Inform 2019 Apr;124:6-12. [doi: 10.1016/j.ijmedinf.2019.01.004] [Medline:
30784428]
Raghavan P, Chen JL, Fosler-Lussier E, Lai AM. How essential are unstructured clinical narratives and information fusion
to clinical trial recruitment? AMIA Jt Summits Transl Sci Proc 2014;2014:218-223 [FREE Full text] [Medline: 25717416]
Zadeh L. Fuzzy sets. Inf Control 1965 Jun;8(3):338-353. [doi: 10.1016/s0019-9958(65)90241-x]
Zadeh L. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1978 Jan;1(1):3-28. [doi:10.1016/0165-0114(78)90029-5]
Zimmermann H. Fuzzy set theory. WIREs Computational Statistics 2010 Apr 16;2(3):317-332. [doi: 10.1002/wics.82]
Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern
1973 Jan;3(3):32-57. [doi: 10.1080/01969727308546046]
Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Comput Geosciences 1984 Jan;10(2-3):191-203.
[doi: 10.1016/0098-3004(84)90020-7]
Principles of pattern recognition I (introduction and uses). NPTEL. 2014. URL: https://nptel.ac.in/courses/106106046[accessed 2022-11-10]
Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care
database. Sci Data 2016 May 24;3:160035. [doi: 10.1038/sdata.2016.35] [Medline: 27219127]
Nakaya J, Koji S, Hiroshi T. Condensed cross-clinical knowledge. Int J Computer Sci Network Secur 2006;6(7A):6-11.
Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv Preprint posted
online on September 7, 2013. [doi: 10.48550/arXiv.1301.3781]
Wang Y, Liu S, Afzal N, Rastegar-Mojarad M, Wang L, Shen F, et al. A comparison of word embeddings for the biomedical
natural language processing. J Biomed Inform 2018 Nov;87:12-20 [FREE Full text] [doi: 10.1016/j.jbi.2018.09.008][Medline: 30217670]
Mantilla L, Yari Y. FCM algorithm: analysis of the membership function influence and its consequences for fuzzy clustering.
In: Orjuela-Cañón AD, Lopez J, Arias-Londoño JD, Figueroa-García JC, editors. Applications of Computational Intelligence
Third IEEE Colombian Conference, ColCACI 2020, Cali, Colombia, August 7-8, 2020, Revised Selected Papers. Cham:
Springer; 2021:119-132.
Anam SA, Rahman AMM, Saleheen NN, Arif H. Automatic text summarization using fuzzy C-means clustering. 2018
Presented at: 2018 Joint 7th International Conference on Informatics, Electronics & Vision and 2018 2nd International
Conference on Imaging, Vision & Pattern Recognition; June 25-29, 2018; Kitakyushu, Japan. [doi:10.1109/iciev.2018.8641055]
Sadollah A. Introductory chapter: which membership function is appropriate in fuzzy system? In: Sadollah A, editor. Fuzzy
Logic Based in Optimization Methods and Control Systems and Its Applications. London: IntechOpen Limited; 2018:1.

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Han et al

Hess AS, Hess JR. Principal component analysis. Transfusion 2018 Jul;58(7):1580-1582. [doi: 10.1111/trf.14639] [Medline:
29732564]
Iqbal E, Mallah R, Rhodes D, Wu H, Romero A, Chang N, et al. ADEPt, a semantically-enriched pipeline for extracting
adverse drug events from free-text electronic health records. PLoS One 2017;12(11):e0187121 [FREE Full text] [doi:10.1371/journal.pone.0187121] [Medline: 29121053]

Abbreviations
DNN: deep neural network
FCM: fuzzy c-means
LASSO: least absolute shrinkage and selection operator
MIMIC-III: Marketplace for Medical Information in Intensive Care III
NCK: Normalized Clinical Knowledge
PCA: principal component analysis

Edited by A Mavragani; submitted 13.04.22; peer-reviewed by K Ding, B Ru; comments to author 15.09.22; revised version received
29.09.22; accepted 24.10.22; published 18.11.22
Please cite as:Han F, Zhang Z, Zhang H, Nakaya J, Kudo K, Ogasawara K
Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and GaussianMembership
JMIR Form Res 2022;6(11):e38677
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PMID:©Feng Han, ZiHeng Zhang, Hongjian Zhang, Jun Nakaya, Kohsuke Kudo, Katsuhiko Ogasawara. Originally published in JMIR
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