Role Of Big Data & (COPD) Phenotypes And ML Cluster Analyses – Potential Topics For PhD Scholars - Phdassistance

1. 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 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 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). help advance this using genomic, 1 Copyright © 2021 PhdAssistance. All rights reserved

2. 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, transcriptomics, proteomics, 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). other metabolomics, Omics (e.g., and 2 Copyright © 2021 PhdAssistance. All rights reserved

3. 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, 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. comorbidity and 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 3 Copyright © 2021 PhdAssistance. All rights reserved

4. 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 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. 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 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, 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. Journal, 55(1). on Information Consortium. (2021). S., Fakhimi, M., European Respiratory 4 Copyright © 2021 PhdAssistance. All rights reserved