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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, info@phdassistance.com

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

1. Bodduluri, S., Nakhmani, A., Reinhardt, J. M., Wilson, C. G., McDonald, M. L., Rudraraju, R.,

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),

e132781.

2. Corlateanu, A., Mendez, Y., Wang, Y., Garnica, R. D. J. A., Botnaru, V., & Siafakas, N. (2020).

Chronic obstructive pulmonary disease and

phenotypes: a state-of-the-art. Pulmonology, 26(2),

95-100.

3. DeMeo, D. L. (2021). Sex and Gender Omic biomarkers in men and women with COPD:

Considerations for precision medicine. Chest.

4. Kim, S., Lim, M. N., Hong, Y., Han, S. S., Lee, S. J., & Kim, W. J. (2017). A cluster analysis of

chronic obstructive pulmonary disease in dusty

areas cohort identified three subgroups. BMC

pulmonary medicine, 17(1), 209.

5. Kothalawala, D. M., Murray, C., Simpson, A., Custovic, A., Tapper, W. J., Arshad, S. H., … &

STELAR/UNICORN Consortium. (2021).

Development of Childhood Asthma Prediction

Models using Machine Learning Approaches.

medRxiv.

6. Levy, J., Álvarez, D., Del Campo, F., & Behar, J. A. (2021). Machine learning for nocturnal

diagnosis of chronic obstructive pulmonary disease

using digital oximetry biomarkers. Physiological

Measurement.

7. Ma, X., Wu, Y., Zhang, L. et al. Comparison and development of machine learning tools for the

prediction of chronic obstructive pulmonary

disease in the Chinese population. J Transl Med 18,

146 (2020).

8. Nikolaou, V., Massaro, S., Fakhimi, M., Stergioulas, L., & Price, D. (2020). COPD

phenotypes and machine learning cluster analysis:

A systematic review and future research agenda.

Respiratory Medicine, 106093.

9. O’Brien, E., Sciurba, F. C., & Bon, J. (2020). COPD Phenotyping. In Precision in Pulmonary,

Critical Care, and Sleep Medicine (pp. 225-239).

Humana, Cham.

10. Pikoula, M., Quint, J. K., Nissen, F., Hemingway, H., Smeeth, L., & Denaxas, S. (2019). Identifying

clinically important COPD sub-types using data-

driven approaches in primary care population

based electronic health records. BMC medical

informatics and decision making, 19(1), 1-14.

11. Silverman, E. K. (2017). Big Data and Network Medicine in COPD. In COPD (pp. 321-332).

Springer, Berlin, Heidelberg.

12. Tang, H. H., Sly, P. D., Holt, P. G., Holt, K. E., & Inouye, M. (2020). Systems biology and big data in

asthma and allergy: recent discoveries and

emerging challenges. European Respiratory

Journal, 55(1).

13. Toews, M., Wachinger, C., Estepar, R. S. J., & Wells, W. M. (2015, June). A feature-based

approach to big data analysis of medical images.

In International Conference on Information

Processing in Medical Imaging (pp. 339-350).

Springer, Cham.

14. Zucchi, J. W., Franco, E. A. T., Schreck, T., e Silva, M. H. C., dos Santos Migliorini, S. R.,

Garcia, T., … & Tanni, S. E. (2020). Different

Clusters in Patients with Chronic Obstructive

Pulmonary Disease (COPD): A Two-Center Study

in Brazil. International Journal of Chronic

Obstructive Pulmonary Disease, 15, 2847.