role of big data & (copd) phenotypes and ml cluster analyses – potential topics for phd scholars -...
DESCRIPTION
Chronic obstructive pulmonary disease (COPD), a leading cause of death worldwide, is a heterogeneous and multisystemic condition. It includes diseases like asthma, emphysema and chronic bronchitis (Nikalaou 2020). It is marked by persistent respiratory symptoms and restricted airflow caused by airway and/or alveolar abnormalities. Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher. Learn More: https://bit.ly/3fYBn4W Contact Us: Website: https://www.phdassistance.com/ UK NO: +44–1143520021 India No: +91–4448137070 WhatsApp No: +91 91769 66446 Email: [email protected]TRANSCRIPT
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Role of Big Data & Chronic Obstructive
Pulmonary Disease (COPD) phenotypes and
ML cluster analyses – potential topics for PhD
Scholars
Dr. Nancy Agnes, Head, Technical Operations Phdassistance, [email protected]
In-Brief
Chronic obstructive pulmonary disease (COPD), a
leading cause of death worldwide, is a heterogeneous
and multisystemic condition. Growth and application
of Machine Learning (ML) algorithms in Medical
Research can potentially help advance this
classification procedure. Scope of ML algorithms was
explored to identify the heterogeneity of certain
conditions. Mathematical models are being developed
Keywords: COPD, phenotypes, asthma, Machine
Learning Algorithm, Big Data Analytics, cluster
analysis, statistical analysis, Machine Learning in
Medical Research, PhD Big Data analysis Help,
COPD phenotypes and Machine Learning, Clinical
Phenotypes of COPD, PhD Dissertation Writing Help
I. INTRODUCTION
Chronic obstructive pulmonary disease (COPD), a
leading cause of death worldwide, is a heterogeneous
and multisystemic condition. It includes diseases like
asthma, emphysema and chronic bronchitis (Nikalaou
2020). It is marked by persistent respiratory symptoms
and restricted airflow caused by airway and/or alveolar
abnormalities. Significant exposure to harmful particles
or fumes is usually the cause of these abnormalities
(Corlateanu 2020). To understand this condition better,
physicians have classified patients into phenotypes
based on symptomatic features, including symptom
severity and history of exacerbations. The growth and
application of machine learning (ML) algorithms in
Medical Research can potentially help advance this
classification procedure (Nikalaou 2020). This review
summarizes the use of machine learning algorithms and
cluster analyses in COPD phenotypes.
II. APPLICATION OF MACHINE LEARNING -
RECENT RESEARCH
The last decade has seen substantial growth in the use
of Machine Learning in Medicine and Research. The
scope of ML algorithms was explored to identify the
heterogeneity of certain conditions. Mathematical
models are being developed using genomic,
transcriptomic, and proteomic data to predict or
differentiate disease phenotypes (Tang 2020).
COPD phenotypic classification has progressed from
the classic phenotypes of emphysema, chronic
bronchitis, and asthma to a plethora of phenotypes that
represent the disease's heterogeneity. Over the last 10
years, new imaging modalities, high-performance
systems for protein, gene, and metabolite assessment,
and integrative approaches to disease classification
have contributed to the identification of a variety of
phenotypes (O'Brien 2020).
Boddulari et al. conducted a Deep Learning and
Machine Learning based analysis using spirometry data
to identify the structural phenotypes of COPD. The
study was conducted on 8980 patients and applied
techniques like random forest and full convolutional
network (FCN). They demonstrated the potential of
machine learning approaches to identify patients for
targeted therapies (Bodduluri 2020). In another study,
researchers evaluated the possible clinical clusters in
COPD patients at two study centres in Brazil. A total
number of 301 patients were included in this study and
methods like Ward and K-means were applied. They
were able to identify four different clinical clusters in
the COPD population (Zucchi 2020).
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Table 1: Recent research on application of machine learning in COPD
Network-based methods have also been used to study
biomarkers of COPD. Sex-specific gene co-expression
patterns have been discovered using correlation-based
network approaches. PANDA (Passing Attributes
between Networks for Data Assimilation) reported sex-
specific differential targeting of several genes, with
mitochondrial pathways being enriched in women
(DeMeo 2021).
III. BIG DATA - ROLE IN COPD ANALYSISBF
The application of Big Data in the Study of heterogenic
conditions is of utmost importance. Analysis of large
amounts of data at once using computing techniques
can help in better understanding of complex diseases
like COPD. Genetics, other Omics (e.g.,
transcriptomics, proteomics, metabolomics, and
epigenetics), and imaging are all vital sources of big
data in COPD study. COPD Genetic Research has
already produced a large amount of Big Data. Another
important source of Big Data in COPD research is
imaging, which is usually done with chest CT scans.
Network science offers methods for analyzing big data
(Silverman 2020). Projects like COPD Gene (19,000
lung CT scans of 10,000 people) provide unprecedented
opportunities to learn from massive medical image sets
(Toews 2015).
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A research undertaken in England signified the
importance of Big Data and Machine Learning in
COPD. The researchers successfully sub-classified
COPD patients into five clusters based on the
demography, risk of death, comorbidity and
exacerbations. They applied cluster analysis methods
on large-scale electronic health record (EHR) data
(Pikoula 2019).
IV. FUTURE SCOPE
The appropriate application of large medical datasets or
big data and machine learning analysis can play a vital
role in the improving management of COPD. The
adoption of these techniques can further facilitate the
classification of individuals with different responses to
therapy.
Fig.1: Use of machine learning algorithms in COPD
That can also lead to personalized therapy for patients
with COPD. To conclude, ML algorithms and big data
hold the potential to change the prognosis and
management of COPD. However, more elaborated
research projects are needed to establish the application
of these tools.
REFERENCES
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Jaeger, B. C., Bhakta, N. R., Castaldi, P. J.,
Sciurba, F. C., Zhang, C., Bangalore, P. V., &
Bhatt, S. P. (2020). Deep neural network analyses
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of spirometry for structural phenotyping of chronic
obstructive pulmonary disease. JCI insight, 5(13),
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2. Corlateanu, A., Mendez, Y., Wang, Y., Garnica, R. D. J. A., Botnaru, V., & Siafakas, N. (2020).
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