PowerPoint Presentation
Role of Big Data & Chronic Obstructive Pulmonary Disease
(COPD) Phenotypes and ML Cluster Analyses – Potential Topics for
PhD Scholars
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Groupwww.phdassistance.com
Email: [email protected]
In Brief
Introduction
Application of machine learning - Recent research Big data -
Role in COPD analysisbf:
Conclusion
Outline
TODAY'S DISCUSSION
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 using genomic, transcriptomic, and proteomic data to
predict or differentiate disease phenotypes.
In-Brief
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).
Contd....
Introduction
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.
Thelastdecadehasseensubstantialgrowthinthe 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).
Contd....
Application of machine learning - Recent research
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).
Contd....
Boddulari et al. conducted a Deep Learning and Machine Learning
based analysis
using spirometry data to identify the structural phenotypes of
COPD.
Thestudywasconductedon8980patientsandappliedtechniqueslikerandom
forest and full convolutional network (FCN).
Theydemonstratedthepotentialofmachinelearningapproachestoidentify
patients for targeted therapies (Bodduluri 2020).
Contd....
Inanotherstudy,researchersevaluatedthepossibleclinicalclustersinCOPD
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.
TheywereabletoidentifyfourdifferentclinicalclustersintheCOPDpopulation
(Zucchi 2020).
Contd....
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).
TheapplicationofB ig DataintheStudyofheterogenic 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.
Contd....
Big data - Role in COPD Analysis
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).
A research undertaken in England signified the importance of B
ig Data and Machine L earning 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).
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.
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.
Future Work
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