Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
Revealing the trends in the academic landscape of the health care system
using contextual topic modeling
1School of computer science and Engineering, Nanjing University of Science and Technology
Muhammad Inaam ul haq 1, Qianmu Li 1
Corresponding authors: Muhammad Inaam ul Haq (Minaamulhaq1@hotmail.com) and Qianmu Li
(qianmu@njust.edu.cn)
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© 2023 Chinesische Akademie der Wissenschaft. Veröffentlicht unter einer Creative Commons Namensnennung 4.0 International (CC
BY 4.0) Lizenz.
Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
Revealing the trends in the academic landscape of the health care system
using contextual topic modeling
Abstrakt
The health care system encompasses the participation of individuals, groups, agencies, Und
resources that offer services to address the requirements of the person, Gemeinschaft, Und
population in terms of health. Parallel to the rising debates on the healthcare systems in relation
to diseases, treatments, Interventionen, medication, and clinical practice guidelines, the world is
currently discussing the healthcare industry, technology perspectives, and healthcare costs. To
gain a comprehensive understanding of the healthcare systems research paradigm, we offered
a novel contextual topic modeling approach that links up the CombinedTM model with our
healthcare Bert to discover the contextual topics in the domain of healthcare. This research
work discovered 60 contextual topics among them fifteen topics are the hottest which include
smart medical monitoring systems, causes, and effects of stress and anxiety, and healthcare
cost estimation and twelve topics are the coldest. Darüber hinaus, thirty-three topics are showing in-
significant trends. We further investigated various clusters and correlations among the topics
exploring inter-topic distance maps which add depth to the understanding of the research
structure of this scientific domain. The current study enhances the prior topic modeling
methodologies that examine the healthcare literature from a particular disciplinary perspective.
It further extends the existing topic modeling approaches that do not incorporate contextual
information in the topic discovery process adding contextual information by creating sentence
embedding vectors through transformers-based models. We also utilized corpus tuning, Die
mean pooling technique, and the hugging face tool. Our method gives a higher coherence score
as compared to the state-of-the-art models (LSA, LDA, and Ber Topic).
Schlüsselwörter: Contextual topic modeling, health care Bert, content analysis, health care system
1. Einführung
Healthcare systems are intricate. These are composed of various interrelated parts. The World
Health Organization (WHO) says a health system is “all institutions, Menschen, and actions whose
primary aim is to promote, restore, or maintain health.” They usually include both rural and
urban areas, public and private systems, formal/allopathic, and informal/traditional methods of
providing healthcare, as well as being at the national level, giving them a tremendous scope.
(Martins et al., 2021).
There are many more functions that health systems do in society in addition to providing
healthcare and other treatments to maintain or improve health. They aid in preventing financial
fallout from illness and medical costs for homes. It’s imperative to keep in mind that health
systems contribute to society’s economy. (Sachs, 2001). The health system is a sector of the
economy that provides employment, revenue, and business opportunities for many health
workers and enterprises. Zum Beispiel, there is some research that suggests that a population’s
health may have an impact on economic productivity. A wider range of societal norms and
values are established through health systems, which are also social and cultural institutions.
(Gilson, 2003). Health systems and the broader environment interact dynamically. Because of
their diffuse nature and usually porous borders, health systems must include the social,
politisch, and economic environment while assessing their structure and efficacy.
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Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
Darüber hinaus, the health systems are locations where actors with various wants and desires
compete and argue. Setting health priorities, funding health systems, and allocating resources
within the system are all contentious issues. The place of the state and the market within health
Systeme, as well as the function that a health system should serve in society, are frequently the
subject of ideological and political disagreement. These various facets of the complexity of
health systems are rarely addressed simultaneously and are transdisciplinary. The fact is that
numerous disciplinary perspectives, such as those of history, economics, medicine,
epidemiology, Politik, law, ethics, anthropology, and sociology, are necessary to fully study
and comprehend health systems (Martins et al., 2021).
The researchers in different studies applied bibliometric methods to analyze the healthcare
Literatur (Jalali et, al.,2019; Rejeb et. al, 2021) or extract latent patterns from the scientific
literature on various subjects of the healthcare system (Ali & Kannan, 2022; Mustakim et
al.,2021; Dantu, Dissanayake & Nerur, 2021). Zum Beispiel, Ali et al. describe a mapping of
the research on healthcare operations and supply chain management. Dantu et al. states the
exploratory analysis of research on IoT in healthcare. These studies are limited to a specific
disciplinary viewpoint and do not show the holistic picture of healthcare research. Darüber hinaus,
these approaches do not capture the context of the discussion. In the current study through the
application of computational methods and advanced topic modeling tools, we capture the
context of the research so that the topics are more semantically understandable. This goal is
achieved by utilizing a contextual word embedding-based topic modeling method. It uses
sentences as the elementary unit of analysis for creating embeddings. Combining
computational methods with qualitative data analysis, we provided highly objective, coherent,
superior, and meta-analytical insight into current research on healthcare systems. This study’s
overall technical and theoretical contributions can be illustrated as follows:
(ich) We developed a novel contextual topic modeling approach that incorporates corpus
tuning and mean pooling techniques to design healthcare Bert which we link up
with CMT(CombinedTM) to generate the contextual topic modeling in the domain
of healthcare care.
(ii) We compare our model with LSA, LDA, and Ber Topic. Our model achieves a
maximum coherence score as compared to these state-of-the-art models.
(iii) This study performs the quantitative assessment of scientific literature in the
domain of health care in terms of contextual topics and classified the topics into hot
and cold categories on the basis of p-values. We also investigated topic clusters and
correlations by exploring inter-topic distance maps.
(iv) We also elaborated the top cited studies qualitatively to cross-validate the topic
themes which enhance the significance of our findings.
The rest of the article is divided into six sections. The first section describes the
introduction. The related work is covered in the second section, and the materials and
methods are covered in depth in the third section. Results and discussions are covered in
the fourth section, followed by a conclusion in the fifth section.
2. Literature review
Hier, we discuss (ich) the top-cited works on the healthcare system as well as (ii) related work
on topic modeling approaches.
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Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
2.1 Healthcare systems
This section covers some overviews of highly cited research on healthcare systems. Significant
research generally deals with diseases, diagnoses, Interventionen, treatments, and other related
Fächer. In the context of diseases, the scholars focus on heart disease stroke, congenital heart
Krankheit, and other vascular diseases (Marelli et al., 2007; Roger et al., 2012; Heidenreich et
al.,2013; Mozaffarian et al.,2015). Regarding healthcare interventions, scholars emphasize
various subjects such as the acceptability of the interventions, evaluation of interventions,
behavioral interventions, and care transition interventions (Liberati et al.,2009; Shea et
al.,2014). The most cited research also analyzes medication effectiveness (Kakkar et al., 2008)
and stigma as the cause of health inequality, service utilization of lifetime mental disorder,
cultural competence in the delivery of healthcare services, and patient perception of hospital
care (Merikangas et. al., 2011; Hatzenbuehlere et. al,2013).
The healthcare industry is undoubtedly the most significant of all the sectors that have profited
from technological adoption. As a result, it eventually raised the standard of living and
contributed to several life savings. The research scholars developed various tools and
techniques to automate the various operations and tasks from a healthcare perspective. Für
Beispiel, a 3D slicer, a clinical research tool similar to a radiology workstation (Fedorov et al,
2012). WSN technologies have various applications in the health sector like sensor-integrated
devices which can monitor human activity such as pressure, temperature, and strain. It also
provides monitoring facilities through contextual information that minimizes the caregiver’s
needs (Alemdar & Ersoy, 2010; Trung & Lee, 2016). Zusätzlich, the IoT-enabling solutions
based on a WSN, RFID, and mobile technology can monitor patients, personnel, and healthcare
devices are another application of technology for healthcare services (Catarinucci et al.,2015).
Material sciences also have a significant contribution to the healthcare industry. Zum Beispiel,
electrospun nanofibers can be used for membrane preparation (Ramakrishna et al.,2006). Silk-
molded electronic skin can monitor the psychological signals of human beings (Wang et al.,
2014). From a healthcare perspective, a large amount of data is generated that can be processed
using machine learning and deep learning techniques. It advances healthcare research and
improves human health (Ludvigsson et al., 2009; Miotto et al.,2018). Beyond these areas, Die
physician acceptance of telemedicine technology and cell phone-based interventions (voice,
text messages) are being evaluated as an alternative to ordinary business settings (Chau & Hu,
2002; Krishna, Boren & Balas, 2009). Zusätzlich, the usage of technology in the healthcare
system positively impacts hospital revenue and the quality of services delivered to patients
(Devaraj & Kohli, 2003). Video conferencing technology has also been applied in healthcare
to train primary care providers to treat complex diseases like HCV infection, which increase
the patient-treatment ratio (Arora et al.,2011).
Regarding healthcare costs and related subjects, researchers pointed out various factors that
increase or decrease healthcare costs. Zum Beispiel, medication adherence, higher medical
adherence decreases patient hospitalization (Ho, Bryson, & Rumsfeld, 2009; Sokol et al.,
2005). The implication of patient follow-up intervention (Jack et al.,2009) and fall-related
injuries can overcome re-hospitalization risk and expenditure (Stevens et al., 2006; Taylor,
2017; Reginster & Burlet, 2006). Surgical site infections (SSIs) are one of the major
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Data Intelligence Just Accepted MS.
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contributors to healthcare-associated infections and contribute significantly to the damage in
medical care through the over-length of stay at hospitals (Zhan, & Müller, 2003; Hidron et
al.,2008; Zimlichman et al.,2013). Infections such as; Clostridium difficile infection and
antibiotic-resistant bacteria threaten the healthcare system as these are the cause of various
Todesfälle (Angus et al.,2004; Klevens et al., 2007; Lessa et al.,2015; Cassini et al.,2019).
daher, the deployment of a surveillance system can provide an estimate of the burden of
the infection. Zusätzlich, prevention activities along with surveillance can avoid infections
and overcome the burden of extra costs in the healthcare system (Magill et al.,2014). Cancer
imposes various health and economic burdens in terms of its treatment which reflects a
substantial increase that highlights the importance of cancer prevention efforts, which may
result in future savings to the healthcare system. daher, research recommends early cancer
detection and treatment for effective cancer control (Guy Jr et al.,2015; Siegel et al., 2019).
The most cited themes also highlight the clinical practice guidelines frequently used as
recommendations (Harris et al.,2001) such as guidelines related to diagnosis, prevention,
intervention, treatment, and patient safety or care (Kimiko & Lowenstein,1985; Boyce, &
Pittet, 2002; Chinn & Sehulster, 2003; Tablan et al.,2004; Jensen et al.,2005; Barlow et al.,
2007; Erasmus et al.,2010; McKhann et al., 2011; McAlindon et al., 2014; Muraro et al.,2014).
2.2 Topic modeling
The group of algorithmic machine learning techniques used in the field of text mining is called
probabilistic topic models. These models look for structural patterns within a corpus to extract
semantic data. The topic templates create word clusters representing the major subjects in a
given corpus. These methods offer an automatic method of locating common topics in the
papers that are being displayed in this manner. Topic modeling can be performed using various
approaches that employ algorithms like NMF, LSA LDA, and clustering employing the K-
means or Ward’s method used for hierarchical clustering (Gurcan et al., 2020; Principe et. al,
2021).
A variety of statistical and probabilistic approaches are used in language modeling (LM) Zu
estimate the likelihood that a given string of words will appear in a sentence. Language models
examine the corpora of text data to provide a foundation for their word predictions. Diese
models are more efficient as compared to other approaches as they take into account the
meaning and semantics of words and sentences as well as the relationship between words.
Zusätzlich, by using this strategy, we were able to achieve the highest level of semantic
integrity within each topic, enhancing the topics’ relevance and differentiating them from one
another (Koroteev,2021). Darüber hinaus, google created a transformer-based machine learning
language processing called Bidirectional Encoder
method for pre-training natural
Representations from Transformers Known as BERT. It was developed and released by Google
employees Jacob Devlin and his team in 2018 (Devlin et al.,2018).
In the literature, we found various approaches that focused on the analysis of scientific
discourses. The existing studies mostly concentrated on using bibliometric techniques (Jalali
et al.,2019; Rejeb et al., 2021), Latent Dirichlet Allocation (LDA) based topic modeling
Techniken (Principe et al, 2021; Cho,2019), or qualitative content analysis (Kim et al, 2021).
The other qualitative methods also applied by the researchers in the scientific trajectory
analysis include systematic mapping reviews, critical reviews, and narrative reviews (Pare &
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Data Intelligence Just Accepted MS.
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Kitsiou 2017). Researchers primarily developed keywords-based analysis techniques that do
not usually capture the context of the discussion. The applications of traditional methods such
as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis are difficult given
the high dimensionality of massive data. The problem also exists of unclear topics caused by
the sparse distribution of topics (Shi et al., 2019). Saheb et al. (2022) propose a context-based
topic modeling approach. It integrates the general Bert model LDA and K-means clustering to
contextually analyze research articles. The generalized Bert model is less efficient in detecting
cluster quality and Kmeans is inefficient in generating clusters of data which has outliers. To
solve the above challenges, we applied contextual topic modeling (Basmatkar & Maurya, 2022)
in the domain of health care. We tuned the specialized Bert model (Medical-Bio-Bert2) on the
corpus of healthcare research articles and then added a mean pooling layer on top of it giving
us a novel HealthcareBert which we link with the CombinedTM (CTM) model to develop a
novel contextual topic model. This strategy improves the coherence score, giving more
accurate embeddings and resulting contextualized topics. We also compared our model with
the state of art LSA, LDA, and Ber Topic models. Our model outperforms the existing models.
We further investigated the topic trends and correlations among the contextualized topics to
add more dimension to understanding the healthcare landscape.
3. Materials & Methoden
In diesem Abschnitt, we discuss the data collection, its pre-processing, and the methodology for the
contextual topic modeling procedure in detail.
3.1 Data collection
We selected Scopus as a data source and the following query: TITLE-ABS-KEY ((healthcare
system* OR health care system* OR Health-care system* OR healthcare* OR health care* OR
Health-care*)) AND (LIMIT-TO (SRCTYPE, “j”)) AND LIMIT-TO (DOCTYPE, “ar”)) AND
(LIMIT-TO (LANGUAGE, “English”)) is executed in this database on 30th November 2022.
As a result, we obtained 29600 records having publication dates from 2000 Zu 2022. Wir
removed duplicates and empty abstract articles from the dataset; the rest have 28036 records.
3.2 Pre-processing
After the data collection process was finished, pre-processing of the data was done before
modeling to increase the data quality. The text in the data set was first divided into tokens using
word tokenization. Then lowercase was applied to the tokens. The text was cleaned up by
getting rid of the numerals, punctuation, and stop words. This was achieved using a typical
English stop word list (n=153). The text is cleaned up using the stemming and lemmatization
Verfahren.
3.3 Healthcare Bert
We develop a transformer-based deep learning model as Healthcare Bert to enhance the
semantic understanding of the topics. We tuned fspanda-Medical-Bio-Bert2 available through
the hugging faces tool on the healthcare corpus and generate an improved Transformers-based
model to provide more accurate context vectors in contextual topic modeling. There are various
pooling methods (z.B., CLS pooling, Mean pooling, Max pooling) for transformer models.
(Zhao et al 2022). We added these three layers on top of HealthcareBert one by one and
computed the coherence score through the CMT model. Jedoch, the addition of mean pooling
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layer gives a higher coherence score so we added this layer on top of our HealthcareBert. (Sehen
Algorithmus -1)
Algorithm-1
Input: Healthcare_papers_abstratcs, Pretrain fspanda-Medical-Bio-BERT2
Output: Healthcare BERT
1. For all Healthcare_papers_abstratcs in the dataset
2.
3.
4.
5.
Tokenize (Healthcare_papers_abstratcs) initializing it with fspanda-Medical-Bio-BERT2
Beispiel
Get the model object fspanda-Medical-Bio-BERT2
Retrain/Tune this model on the Healthcare corpus
Added mean pooling layer
3.4 Topic Models and CombinedTM
Latent Dirichlet allocation (LDA) is an important and widely used probabilistic topic model. Es
is based on a generative process denoted by the equation as follows:
𝑁
𝑝(𝜃, 𝑧, 𝑤|α,β) = 𝑝(𝜃|𝛼) ∏ 𝑝(𝑧𝑛|𝜃)𝑝(𝑤𝑛|𝑧𝑛 , 𝛽 )
𝑛=1
—(ich)
Since Dirichlet prior is not a location-scale family, to solve this issue in ProdLDA the decoder
network is used to approximate the Dirichlet prior 𝑝(𝜃|𝛼) with a logistic normal distribution
given by the equation where µ and ∑ represents the outputs of the encoder network as follows:
𝑝(𝜃|𝛼) ≈ 𝑝(𝜃|𝜇, ∑) = 𝐿𝑁(𝜃|𝜇, ∑)———–(ii)
The encoder network has some disadvantages that it is stuck in a bad local optimum this
problem is addressed using Adam optimizer, batch normalization, and dropout units in the
encoder network. Another difference between LDA and ProdLDA is that 𝛽 is unnormalized
and 𝑤𝑛 is defined as:
(𝜔𝑛|𝛽, 𝜃~𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑖𝑎𝑙(𝜎(𝛽𝜃))—————-(iii)
CombinedTM (CTM) is a contextualized topic model inspired by ProdLDA even though both
of the models use the same hyperparameters. The original CTM model uses SBERT features
in combination with the Bow (Bag of words). In the current study, we replaced SBERT with a
novel Healthcare BERT. We fetched and processed the data set using Panda’s data frame. Der
CombinedTM model is employed that integrates (Bianchi, Terragni & Hovy, 2021)
contextualized embeddings and a bag of words model. Endlich, contextual topics were
generated through CombinedTM, and the sentence embedding vectors were constructed using
the improved healthcare Bert we have developed in the current study as shown in (Fig.1).
3.5 Model Evaluation
For the assessment of the validity of our approach, we design the following criteria (ich)
quantitative assessment (Coherence score) Und (ii) qualitative assessment (highly cited
Literatur).
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(ich)
Quantitative assessment (Coherence Score)
The selection of an optimal model from the list of generated models is a critical task. Human
comprehension depends on the concept of semantic context, and the coherence method makes
an attempt to determine the context between words in a topic. Maximising the coherence score
is crucial because it provides subjects that are easier for humans to understand. This context
cannot be captured by the other matrices (such as perplexity). Infolge, we assess the model’s
performance using the coherence score (Pasquali, 2017). We generated various models with
NEIN. of topics k in the range of (10 Zu 100 with the step of 5) employing healthcare Bert with
CombinedTM and plot the coherence score as (Coherence Cv) corresponding to each model in
both cases. We choose the CombinedTM model that gives the maximum value of coherence
Cv among the lists of generated models.
𝐶𝑜ℎ𝑒𝑟𝑒𝑛𝑐𝑒(𝑉) = ∑
(𝑣𝑖,𝑣𝑗)𝜀𝑉
𝑠𝑐𝑜𝑟𝑒(𝑣𝑖, 𝑣𝑗, 𝜖)
——————-(iv)
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Figure.1: Contextual topic modeling framework
(ii) Qualitative assessment
For a thematic understanding of the discovered topics, we also adopted a qualitative approach
to cross-validate the topic’s themes. In this method, we summarize the highly cited literature
in section (2.1) which facilitates the general assessment of topic themes.
3.6 Topic Trends and popularity measurement
In this study, every topic’s trends are examined, and the posterior distribution is linked to the
year that each document was published. Each document gives a certain topic to it that best
represents its likelihood at that current time. By dividing the total number of papers each year
by the number of papers assigned to this topic, the total number of papers was normalized to
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determine the topic proportion for each topic each year. Following that, the cumulative
proportion was used to calculate the overall popularity. Zusätzlich, the Mann-Kendall trend
test (M-K-test) is used to look for enduring upward or downward patterns in the data gathered
im Laufe der Zeit. It is a non-parametric trend test method that examines discrepancies between earlier
and later data points and is applicable to all distributions. It denotes that when a trend is present,
sign values consistently tend to rise or fall (Wang et al 2020). Each topic’s rising and declining
patterns were recorded using the Mann-Kendall test.
3.7 Inter-topic distances and Topic correlations
We used LDAvis, a visualizing tool for topic models, to aid interpretations of the
contextualized topics. This package plots the topics on a two-dimensional plane which gives
us inter-topic distances, topic clusters, and resulting correlations.
4. Results and discussions
In this study firstly we develop a transformer-based deep learning model to enhance the
semantic understanding of the topics. Google created various transformer-based machine
learning methods for pre-training natural language processing which required large
computational resources. Also, we search first for the most suitable transformer-based model
existing on the hugging face (fspanda-Medical-Bio-BERT2) that can provide word embeddings
for our data set. To improve these word embeddings, we tuned this model further using Google
GPU and Colab Notebook on the local corpus. Weiter, we added a mean pooling layer after
corpus tuning. In this way, we generated a novel model as Healthcare Bert. These word
embeddings are provided as context vectors to the CTM model that combine a bag of words
and word embeddings to generate contextual topics of the corpus. The newly generated word
embeddings also improve the topic’s coherence score of the CTM models. CTM generates a
document term matrix (DTM) which we further investigated using various techniques of
statistics to classify the topics into hot and cold categories. Five subheadings—(ich) models’
evaluation/performance analysis, (ii) contextual topics, (iii) classification of topic trends, Und
(v) inter-topic distances topic clusters, and correlations are used to organize the study’s
Erkenntnisse.
4.1 Models’ Evaluation/performance analysis
We programmed coherence matric (C_v) to compare the proposed model (CMT) with LSA,
LDA, and Ber Topic. Table.1 shows the values of the coherence score (C_v) and no. of topics
(K) applying our healthcare Bert with CTM and Ber Topic. We also computed the coherence
score of other state-of-the-art methods (LSA and LDA) on our data set without our healthcare
Bert. All values of the coherence score computed using different models are analyzed and the
CTM model generated using our novel healthcare Bert with k=60 gives a clear maxima value
and is chosen as an optimal model to report the contextualized topics as shown in (See Table.1).
Table.1 Performance Analysis of LSA, LDA, Ber Topic, and Our Method
Topic No
LSA
LDA
Ber Topic
Our Method
10
15
0.3516
0.4176
0.3363
0.4652
0.4212
0.4183
0.5880
0.5841
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20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
0.3210
0.4592
0.3142
0.4684
0.3119
0.4860
0.3078
0.4873
0.2976
0.4778
0.2932
0.4979
0.2912
0.4763
0.2849
0.4686
0.2827
0.4662
0.2747
0.4754
0.2739
0.4637
0.2674
0.4802
0.2676
0.4399
0.2650
0.4638
0.2601
0.4300
0.2562
0.4384
100
0.2563
0.4472
4.2 Context-based topics
0.3852
0.3781
0.3539
0.3554
0.3482
0.3375
0.3373
0.3414
0.3272
0.3370
0.3297
0.3251
0.3205
0.3294
0.3342
0.3309
0.3332
0.5850
0.5898
0.5831
0.5775
0.5840
0.5873
0.5806
0.5848
0.5978
0.5782
0.5817
0.5875
0.5819
0.5517
0.5608
0.5771
0.5657
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This section lists the contextualized topics that our innovative healthcare Bert and the CTM
model uncovered. The terms of each topic are supplied in (Appendix A) and explained in (Sehen
Tisch 2).
Table.2 Top 20 terms-based topic descriptions
Topic description
Trends Topic description
01: 95 Ci for transplantation
↑↑
31: Asthma and costs
02: 95 Ci testing
↑↑↑↑
32: Drug management and treatment
03: Patient treatment cost analysis ↓
33: Hypertension diabetes and medication
04: Cost gained strategy
05: Diabetes Intervention
06: Health expenditure
↓↓
↓
↓
34: Global financing for public health
35: Technology adoption and its acceptance
36: Nurses’ job satisfaction
07: Resistance in MRSA isolation
and transmission
↓↓↓↓
37: Healthcare IoT
Trends
↓↓↓↓
↑
↓
↑↑
↑
↓
↑
08: Healthcare services
↑↑
38: Surgery and Complications
↑↑↑↑
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09: Costs estimation
↑↑↑
39: Role of nursing as a healthcare professional
↓↓↓
10: 95 Ci for patient disparities
11: Antimicrobial compliance
12: 95 Ci hospital risks
13: Patient treatment risks
↑↑
↓↓
↑↑↑↑
↑↑↑↑
40: Management of organizational processes
41: Side effects of overwork
42: Smart medical monitoring system
43: Coronavirus
Untersuchung
14: Home intervention for patient
care
↓↓↓↓
44: Patient care quality and healthcare
15: Brest cancer
16: Hygiene compliance
↓
↓↓↓
45: Care quality and management
46: Needs of cancer patients
17: Glucose control intervention
↓↓
47: Research on health policy
18: Maternal inequalities
19: Children’s hospitalization
Tarife
20: Healthcare Education
21: HPV vaccination and
immunization
22: Health pandemic and
vaccination
23: Reviews of healthcare
Forschung
24: Infection-associated cost
25: Pneumonia patients
26: Primary healthcare services
27: Patient healthcare cost and
services
28: Asthma
29: Hospital charges and costs
30: Causes and effects of anxiety
and stress
↑
↓
↓
↓
↑↑
↑↑
↓
↓
↑
↓
↓↓
↓
48: Patient care quality
49: Sexual substance and discrimination
50: Healthcare quality indicators
51: Healthcare devices
52: Women’s stigma
53: Healthcare Discrimination
54: Telemedicine technology
55: Physical and emotional effects on quality of
life (QOL)
56: Nursing and ethical policy
57: Consumer documentation
58: Childhood disparities
59: Audit attendance category
↑↑↑
60: Inappropriate audit attendance
↓
↓
↑↑
↑
↑↑
↓↓↓↓
↓
↑↑↑↑
↓
↓
↓
↑
↓
↓
↓↓↓↓
↓↓
↓
↓
↑
↓
↓
Our contextual topic modeling approach generated 60 topics. Among these topics, 15 showed
significant increasing trends. These topics mainly deal with smart medical monitoring systems
(Topic-42), different health pandemics and the importance of vaccination to control them
(Topic-22), causes and effects of stress and anxiety (Topic-30), and health care services
generally (Topic-08). Hot topics also include healthcare cost estimations (Topic-09), patient
treatment risks (Topic-13), research on designing healthcare policy (Topic-47), 95 ci hospital
risks (Topic-12), patient care quality (Topic-44), global financing for public health (Topic-34),
95 ci testing (Topic-02), surgery complications (Topic-38), health care research reviews
(Topic-23),95 ci for patient disparities and transplantation (Topic-10 and Topic-01). Twelve
topics showed significant decreasing trends. These topics covering care quality and
management (Topic-45), Asthma and costs (Topic-28 &Topic-31), Hygiene compliance
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(Topic-16), Telemedicine technology (Topic-54), glucose control intervention (Topic-17),
Antimicrobial compliance (Topic-11), Home intervention for patient care (Topic-14), Cost
gained strategy (Topic-04), Physical and emotional effects on quality of life (QOL) (Topic-
55), the role of nursing as a healthcare professional (Topic-39) and Resistance in MRSA
isolation and transmission (Topic-07).
Beyond these topics, contextual topic modeling also discovered thirty-three more topics which
were not shown significant increasing or decreasing trends but revealed various important
subjects of the healthcare landscape. Zum Beispiel, infection-associated cost (Topic-24), patient
treatment cost analysis (Topic-03), hospital charges and cost (Topic-29), Patient healthcare
cost and services (Topic-27), children hospitalization rates (Topic-19), childhood disparities
(Topic-58), Healthcare discrimination (Topic-53), Sexual substance and discrimination (Topic-
49), HPV vaccination and immunization (Topic-21), investigation for coronavirus (Topic-43),
Hypertension diabetes and medication (Topic-33), diabetes intervention (Topic-05), drug
management and treatment (Topic-32), needs of cancer patients (Topic-46), pneumonia
Patienten (Topic-25), nursing and ethical policy (Topic-56), nurses’ job satisfaction (Topic-36),
patient care quality (Topic-44&Topic-48), primary healthcare services (Topic-26), Gesundheit
expenditure (Topic-06), consumer documentation (Topic-57), management of organizational
processes (Topic-40), Healthcare quality indicators (Topic-50) inappropriate audit attendance
and audit category (Topic-60 &Topic-59). Regarding education and technology, CTM
discovered various topics like healthcare education (Topic-20), technology adoption and its
acceptance (Topic-35), and Healthcare IoT and devices (Topic-51 & Topic-37). Beyond these
topics, some deal deals with women like women’s stigma (Topic-52), Brest cancer (Topic-15),
and maternal inequalities (Topic-18).
For a profound understanding of the discovered topics, we also elaborated on the top-cited
articles of the corpus in section (2.1) that can cross-validate the themes of the mostly contextual
topics. Topic 42, Topic 51, Topic 37, Topic 54, and Topic 35, zum Beispiel, can be supported
in light of “Paragraph 2.” (par.2). Both sections disused the applications of technology for
healthcare. Another example would be the topics (Topic-24, Topic-04, Topic-38, Topic-29,
Topic-27, Topic-31, Topic-03, Topic-9) are positioned with (par.3). These units mainly
focused on cost and other relevant subjects like surgery and infections. Ähnlich, we can also
cross-validate the rest of the topics. Another perspective of this study is that since we already
encoded the context of topics in the modeling, the discovered topics are more coherent and
self-explanatory (See Table.2).
4.3 Classification of topic trends
Based on the p-value, different groups are created for the trends of the contextualized topics.
These classes were described in terms of arrow symbols. Zum Beispiel, the arrow symbol ↑ (↓)
showed an increasing (decreasing) trend, but not significant (if p > 0.05), and other arrow
symbols like ↑↑ (↓↓), ↑↑↑ (↓↓↓), ↑↑↑↑ (↓↓↓↓) showed a significantly increasing (decreasing)
trend (if p < 0.05, p < 0.01, and p < 0.001, respectively as shown in (See Table.1). The annual
distributions of these topics are depicted from the graphs (See Figure 3 and 4). The fifteen
topics showed significantly increasing trends, twelve topics showed significantly decreasing
trends thirty-three topics do not show significantly increasing or decreasing trends.
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4.4 Inter-topic distances topic-clusters and correlations
We analyzed the inter-topic distance map of the most fitted contextualized topic model with
(K=60). Figure 2 gives us three important clusters of the topics. Cluster-1 shows thirteen topics,
and cluster-2 is dense and consisted of twenty-three topics. Cluster-3 consists of ten topics. We
analyzed the different topics residing in these clusters which give important patterns, and
correlations among the topics and various inter-topic research directions as follows:
The most significant topics covered under cluster-1 mainly focused on statistical analysis (95-
CI) for different aspects of medical care, risk factors, associated cost, and re-hospitalization in
severe medical conditions. Cluster-2 is dense and is further composed of various sub-groups.
In the first group generalized topics (23, 32, 36, 46, 50) narrate the reviews on different areas
of healthcare like job satisfaction, drug management, the need for cancer patients, and
healthcare quality indicators.
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Figure: 2. Inter-topic distance and topic clusters
The topics in the second group (48,49,53) correlate due to discrimination factors in healthcare
generally and sexual substance discrimination
topics
(4,11,16,21,22,55) point out the pandemic (covid-19), its effects, symptoms, antimicrobial
compliance, the importance of hand hygiene, estimation of the cost of vaccination, and the
immunity of the people against various infections. The group-4 (topics 6,14,15,18,41) is
covering health expenditure and home interventions for many health impairments like breast
cancer, migraine, and maternity in different economic situations. It also clarifies the side effects
of overwork, especially in women. Group 5 (topics 3,9,17,30) focuses on cost estimation and
in particular. The group-3
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Figure 3. Graphical representations of topics (1-30) trends from 2000 to 2022
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Figure 4. Graphical representations of topics (31-60) trends from 2000 to 2022
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patient treatment in different diseases, especially in glucose control, drugs, and insulin.
Additionally, it highlights the effects of inappropriate audit attendance cost estimation.
Cluster-3 (topics 20,26,34,35,39,40,42,45,47,52) elaborates on the significance of healthcare
education, nursing, geographic organizational management of finance in the healthcare sector,
uses, and application of smart medical appliances in healthcare units. It points out the
discomforts of women’s stigma ethically. This cluster also shows its findings on research in
healthcare policies, care, quality, and clinical management, provision of primary healthcare
services, and technology adoption in the treatment process.
5 Conclusion
In this study, we developed a context-based topic modeling approach that uses a transformer-
based deep learning model to enhance the semantic understanding of the topics. We developed
a novel Healthcare Bert to provide word embeddings as context vectors. These embeddings are
combined with a bag of words to generate contextual topics of the corpus in context-based
topic modeling. The newly generated word embeddings also improve the topic’s coherence
score. In order to categorize the themes into hot and cold categories, we further explored the
document term matrix (DTM), which CTM generates using statical analysis techniques. This
study also sheds light on the correlation between the topics by plotting them on a two-
dimensional plane with a visualization tool. In this way, various interesting topic patterns and
inter-topic research directions are pointed out. By generating rich sentence embedding vectors
of the corpus under study using transformers-based models, corpus tuning, mean pooling, and
the hugging face tool, this research broadens the existing topic modeling approaches which do
not include contextual information in the topic extraction process. Moreover, it improves the
previous topic modeling methodologies that analyze the healthcare literature from a specific
disciplinary viewpoint. This process has several restrictions and things to think about. We can
look at online databases like Web of Science and PubMed when choosing a data source,
however, the current study solely takes into account papers that are indexed in Scopus. The
current study adds context information to the topics and further gives clustering and topic
correlations analysis, in future studies we may add hierarchical semantic modeling and
temporal perspective in this direction.
Declarations
Availability of data and material.
The data underlying this article is available in [Google Drive], at
https://drive.google.com/drive/folders/1TOmO7VQYnhq-__CIU6Dqk5weFqkYAbH2?usp=sharing
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Appendix A
P-value
0.0065
0.0004
0.355
0.0283
0.653
0.978
0.0004
0.0283
0.0018
0.0047
0.0033
0.0009
Topic
01:95 ci for
transplantation
02:95 ci
testing
03: Patient
treatment cost
analysis
04: cost
gained
strategy
05: diabetes
intervention
06: Health
expenditure
07: Resistance
in mrsa
isolation and
transmission
08: Healthcare
services
09: Costs
estimation
10:95 ci for
patient
disparities
11:
antimicrobial
compliance
12:95 ci
hospital risks
Top Terms
transplantation, transplant,95, ci, crc, hr, hazard, blacks, stage, women, cancer,
hispanics, survival, years, age, whites, hispanic, ses, among, disparities
hiv, aor, tb, ci, 95, testing, tuberculosis, risk, hcv, hcws, among, infected, posit
ive, tested, infection, art, hepatitis, test, exposure, occupational
cost, costs, incremental, per, qaly, utility, placebo, treatment, patient, perspecti
ve, compared, gained, effectiveness, model, total, analysis, patients, trial, year,
life
qaly, cost, gained, strategy, incremental, model, effective, effectiveness, per, s
ensitivity, lifetime, perspective, life, years, screening, costs, year, treatment, sa
vings, analysis
care, diabetes, primary, intervention, based, quality, screening, control, outco
mes, practice, usual, management, self, intervention, controlled, trial, participa
nts, group, effectiveness, practices
households, health, china, expenditures, pocket, services, expenditure, househ
old, payments, rural, utilization, inequality, urban, private, outpatient, children,
insurance, areas, income, spending
isolates, outbreak, mrsa, aureus, resistant, isolation, ca, isolated, transmission,
strains, resistance, hcw, environmental, hcws, infection, staphylococcus, antim
icrobial, spread, tb, contact
hiv, services, health, facilities, healthcare, study, facility, rural, access, care, co
mmunity, maternal, people, workers, district, women, service, delivery, tb, pri
mary
billion, costs, million, indirect, disease, estimates, direct, burden, attributable,
cost, fractures, per, estimated, incidence, year, estimate, total, annual, expectan
cy, countries
95, ci, patients, hispanic, black, race, racial, white, whites, disparities, use, odd
s, ed, ethnicity, likely, opioid, among, blacks, receive, associated
antimicrobial, compliance, hygiene, catheter, icus, hand, antibiotic, infection, s
urveillance, control, hospitals, units, bed, waste, rates, icu, feedback, infection
s, intensive, resistant
95, ci, risk, associated, hospital, rate, study, adverse, days, icu, events, rr, meta
, mortality, studies, catheter, intervention, readmission, outcomes, ratio
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3
Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
0.0001
0.0001
0.1611
0.0013
0.0162
0.7931
0.2904
0.6918
0.0504
0.0151
0.0047
0.615
0.9368
0.383
0.4127
0.0283
0.328
0.0015
0.0001
13: Patient
treatment risks
14: home
intervention
for patient
care
15: Brest
cancer
16: hygiene
compliance
17: glucose
control
intervention
18: Maternal
inequalities
19: children
hospitalization
rates
20: Healthcare
education
21: hpv
vaccination
and
immunization
22: health
pandemic and
vaccination
23: Reviews
on health care
research
24: Infection-
associated
cost
25:
pneumonia
patients
26: Primary
healthcare
services
27: patient
healthcare
cost and
services
28: Asthma
29: Hospital
charges and
costs
30: Causes
and effects of
anxiety and
stress
31: asthma
and costs
patients, risk, hr, hazard,95, claims, treatment, ci, years, failure, matched, thera
py, vs, disease, costs, date, diagnosis, higher, survival, compared
care, patients, home, intervention, group, usual, hf, clinic, primary, palliative,
control, life, hospital, gp, randomized, months, program, groups, patient, telep
hone
cancer, breast, prostate, stage, lung, colorectal, survivors, screening, survival,
crc, women, cervical, treatment, diagnosis, chemotherapy, african, black, diag
nosis, racial, disparities
Hand, waste, hygiene, doctors, compliance, physicians, staff, errors, workers, s
afety, reported, attitude, reporting, knowledge, nurses, training, pharmacists, h
ealthcare, respondents, medical
exercise, group, glucose, control, pressure, intervention, weeks, blood, training
, week, participants, placebo, randomised, self, activity, controlled, randomize
d, difference, trial, groups
maternal, inequalities, health, mortality, birth, neonatal, inequality, factors, out
comes, countries, risks, socioeconomic, antenatal, income, deaths, low, pregna
ncy, 95, ci, women
rates, children, hospitalizations, trends, age, rate, hispanic, injury, uninsured, n
ationwide, hospitals, states, pediatric, blacks, per, medicaid
students, learning, training, practice, skills, education, implementation, eviden
ce, knowledge, educational, change, teaching, development, course, clinical, in
terventions, research, behavior, consensus, nursing
hpv, vaccination, immunization, vaccine, influenza, cervical, uptake, vaccines,
recommendation, screening, women, parents, pregnant, coverage, adolescent,
knowledge, among, crc, cancer, years
pandemic, covid,19,2020, influenza, vaccination,2021, vaccine, vaccines, willi
ngness, workers, sars, coronavirus, hcws, cov,2019, personal, stress, work, pub
lic
Reviews, meta, articles, studies, systematic, search, trials, medline, searched, r
eview, inclusion, outcomes, included, evidence, quality, research, intervention
s, literature, bias, criteria
catheter, cdi, per, infection, associated, days, infections, cost, rates, surveillanc
e, incidence, urinary, nosocomial, icus, 1000, hospital, rate, costs,000, cases
acquired, ca, pneumonia, cdi, onset, patients, aureus, sepsis, children, hospital,
associated, icu, infection, mrsa, resistant, infections, community, admission, re
spiratory, isolates
care, palliative, health, services, primary, dementia, people, community, needs,
mental, service, integrated, home, chronic, access, system, support, manageme
nt, families, disease
care, costs, patients, healthcare, services, cost, medical, study, data, health, tot
al, patient, use, service, claims, hospital, system, related, outpatient, year
ed, asthma, veterans, va, use, care, ptsd, pain, visits, mental, children, primary,
reported, visit, emergency, medical, department, disorders, medication, disord
er
Hospital, charges, costs, los, hospitalizations, stay, length, readmission, admiss
ions, inpatient, readmissions, total, hospitalization, sepsis, cost, nationwide, as
sociated, mortality, ed, admission
anxiety, stress, violence, depressive, depression, ptsd, sleep, burnout, psycholo
gical, work, symptoms, workers, covid, mental, physical, ci, associated, factor
s, risk, disorder
patients, vs, asthma, mean, copd, costs, group, symptoms, severe, months, day
s, 12, disease, pulmonary, per, total, year, one, median, treatment
l
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3
Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
0.290
0.4756
0.0162
0.249
0.8325
0.9195
0.0004
0.0039
0.0537
0.5609
0.0151
0.2673
0.024
0.0005
0.107
0.0001
0.0723
0.634
32: drug
management
and treatment
33:
hypertension
diabetes and
medication
34: Global
financing for
public health
35:
Technology
adoption and
its acceptance
36: Nurses job
satisfaction
37: Healthcare
iot
38: Surgery
Complications
39: role of
nursing as
healthcare
professional
40:
Management
of
organizational
processes
41: Side effect
of overwork
42: Smart
medical
monitoring
system
43:
Coronavirus
Investigation
44: patient
care quality
and healthcare
45: Care
quality and
management
46: Needs of
cancer
patients
47: Research
on health
policy
48: Patient
care quality
49: sexual
substance and
discrimination
treatment, clinical, drug, management, guidelines, drugs, guideline, therapy, re
commendations, therapies, disease, adherence, patients, treatments, medication
, consensus, evidence, pain, may, therapeutic,
hypertension, diabetes, cam, drugs, prevalence, prescription, drug, use, years,
age, women, population, medication, medications, adherence, prescribed, pres
criptions, glucose, 95, among
financing, countries, health, public, sector, government, policy, private, policie
s, china, equity, global, africa, expenditure, spending, country, european, worl
d, funding, universal
data, information, adoption, technology, ehr, healthcare, systems, model, use,
privacy, system, acceptance, electronic, sharing, decision, records, research, us
er, users, used
job, burnout, satisfaction, dimensions, reliability, items, validity, nurses, safety
, instrument, patient, culture, teamwork, nurse, work, nursing, leadership, facto
r, item, quality
smart, scheme, algorithm, network, iot, networks, proposed, internet, remote, d
evices, energy, cloud, security, efficient, accuracy, wireless, secure, prediction,
real, propose
volume, surgeons, surgery, postoperative, undergoing, mortality, surgical, hos
pitals, readmission, patients, hospital, complications, outcomes, procedures, tr
auma, factors, risk, los, covid, day
nurses, nursing, nurse, professional, students, caring, role, practice, roles, ethic
al, working, blackwell, skills, ethics, professionals, team, gps, managers, mem
bers, themes
process, processes, organisational, methodology, organisations, waste, paper, a
pproach, systems, organizational, simulation, theory, value, technology, chain,
supply, decision, managers, complexity, conceptual
productivity, costs, migraine, hrqol, indirect, copd, direct, asthma, disease, imp
airment, pain, work, burden, chronic, life, healthcare, impact, reported, total, r
elated
internet, mobile, remote, medical, smart, information, monitoring, privacy, dev
ices, security, system, telemedicine, secure, communication, iot, cloud, propos
ed, technologies, technology, user
ct, viral, ml, sars, respiratory, cell, ant, syndrome, dose, cov, skin, infectious, v
irus, coronavirus, rapid, epidemic, detected, samples, min, water
patient, care, quality, study, healthcare, research, satisfaction, practice, primary
, nurses, health, nursing, process, using, information, professionals, service, de
sign, support, communication
care, quality, management, clinical, organizations, system, standards, evidence
', guidelines, systems, recommendations, development, new, medicine, based,
stroke, practice, improvement, programs, delivery
Patients, cancer, caregivers, needs, palliative, patient, family, information, pref
erences, life, professionals, care, making, end, communication, families, decisi
on, unmet, support, diagnosis
research, health, social, policy, community, implementation, services, equity, c
ommunities, methods, framework, barriers, approach, cultural, qualitative, peo
ple, evidence, interventions, factors, determinants
Care, quality, patient, hospital, hospitals, performance, Accreditation, satisfact
ion, beds, critical, measures, emergency, safety, nursing, physicians, centered,
improvement, medical, physician, nurse
sexual, substance, cam, discrimination, mental, health, use, veterans, women, s
ervices, abuse, seeking, stigma, among, alcohol, barriers, unmet, reported, diso
rders, men
l
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2
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3
Data Intelligence Just Accepted MS.
https://doi.org/10.1162/dint_a_00217 5
0.1388
0.4145
0.4435
0.123
0.0001
0.0011
0.793
0.559
0.0813
0.1039
0.218
50: health care
quality
indicators
51: Healthcare
devices
52: Women
stigma
53: Health
care
discrimination
54:
telemedicine
technology
55: Physical
and emotional
effects on
quality of life
(QOL)
56: Nursing
and ethical
policy
57: Consumer
documentation
58: Childhood
disparities
59: audit
attendance
category
60:
Inappropriate
audit
attendance
data, ehr, performance, quality, administrative, indicators, electronic, informati
on, hospitals, records, clinical, used, systems, use, patient, using, measures, res
earch, model, predictive
skin, device, pressure, mechanical, detection, energy, monitoring, sensor, fall,
flexible, algorithm, conventional, detect, power, technique, ms, imaging, accur
acy, wearable, human
stigma, women, hiv, experiences, sexual, social, partners, pregnant, themes, su
pport, qualitative, pregnancy, participants, seeking, aids, young, men, depth, b
eliefs, interviews
care, health, discrimination, disparities, ethnic, access, racial, minority, dental,
americans, preventive, cultural, quality, survey, children, insurance, provider,
satisfaction, whites, providers
telemedicine, technologies, digital, market, technology, internet, adoption, sec
urity, requirements, industry, consumers privacy, infrastructure, business, sect
or, integration, users, solutions, networks, consumer
qol, pain, functioning, back, exercise, self, physical, life, cognitive, hrqol, sym
ptom, parents, depression, symptoms, functional, intensity, scale, emotional, a
ctivity, distress
nursing, article, ethical, policy, competence, practice, ethics, workforce, cultur
al, education, leadership, professional, concept, leaders, nurse, political, ways,
must, changing, programs
consumer, legal, documentation, pharmaceutical, consumers, act, food, advanc
e, youth, crisis, inappropriate, assistance, accreditation, continuity, market, suc
cess, travel, organ, encounters, reimbursement
americans, childhood, disparities, minority, oral, ethnic, cardiovascular, africa
n, prevention, morbidity, disease, racial, kidney, minorities, american, disorder
s, populations, include, heart, obesity
audit, attendance, category, cessation, episode, continuity, encounters, consult
ations, employment, netherlands, completion net, class, ms, falls, inappropriate
, intensity, nutrition, knee, preventable,
audit, attendance, inappropriate, cessation, continuity, list, category, waiting, p
reventable, urgent, class, employment, completion, consultations, nutrition, ms
, accreditation, spent, malaria, encounters
l
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2
3
