Latent COVID-19 clusters in patients with chronic respiratory conditions

Wanting Cui; Manuel Cabrera; Joseph Finkelstein

doi:10.3233/SHTI200689

Latent COVID-19 clusters in patients with chronic respiratory conditions

Wanting Cui
, Manuel Cabrera
, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

Original language	English
Title of host publication	Integrated Citizen Centered Digital Health and Social Care
Subtitle of host publication	Citizens as Data Producers and Service co-Creators - Proceedings of the EFMI 2020 Special Topic Conference
Editors	Alpo Varri, Jaime Delgado, Parisis Gallos, Maria Hagglund, Kristiina Hayrinen, Ulla-Mari Kinnunen, Louise B. Pape-Haugaard, Laura-Maria Peltonen, Kaija Saranto, Philip Scott
Publisher	IOS Press BV
Pages	32-36
Number of pages	5
ISBN (Electronic)	9781643681443
DOIs	https://doi.org/10.3233/SHTI200689
State	Published - 23 Nov 2020
Event	EFMI 2020 Special Topic Conference on Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators - Virtual, Online Duration: 26 Nov 2020 → 27 Nov 2020

Publication series

Name	Studies in Health Technology and Informatics
Volume	275
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	EFMI 2020 Special Topic Conference on Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators
City	Virtual, Online
Period	26/11/20 → 27/11/20

Keywords

COVID-19
Chronic lower respiratory diseases
Cluster analysis

Access to Document

10.3233/SHTI200689

Cite this

Cui, W., Cabrera, M., & Finkelstein, J. (2020). Latent COVID-19 clusters in patients with chronic respiratory conditions. In A. Varri, J. Delgado, P. Gallos, M. Hagglund, K. Hayrinen, U.-M. Kinnunen, L. B. Pape-Haugaard, L.-M. Peltonen, K. Saranto, & P. Scott (Eds.), Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators - Proceedings of the EFMI 2020 Special Topic Conference (pp. 32-36). (Studies in Health Technology and Informatics; Vol. 275). IOS Press BV. https://doi.org/10.3233/SHTI200689

Cui, Wanting ; Cabrera, Manuel ; Finkelstein, Joseph. / Latent COVID-19 clusters in patients with chronic respiratory conditions. Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators - Proceedings of the EFMI 2020 Special Topic Conference. editor / Alpo Varri ; Jaime Delgado ; Parisis Gallos ; Maria Hagglund ; Kristiina Hayrinen ; Ulla-Mari Kinnunen ; Louise B. Pape-Haugaard ; Laura-Maria Peltonen ; Kaija Saranto ; Philip Scott. IOS Press BV, 2020. pp. 32-36 (Studies in Health Technology and Informatics).

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title = "Latent COVID-19 clusters in patients with chronic respiratory conditions",

abstract = "The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.",

keywords = "COVID-19, Chronic lower respiratory diseases, Cluster analysis",

author = "Wanting Cui and Manuel Cabrera and Joseph Finkelstein",

note = "Publisher Copyright: {\textcopyright} 2020 The European Federation for Medical Informatics (EFMI) and IOS Press.; EFMI 2020 Special Topic Conference on Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators ; Conference date: 26-11-2020 Through 27-11-2020",

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doi = "10.3233/SHTI200689",

language = "English",

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editor = "Alpo Varri and Jaime Delgado and Parisis Gallos and Maria Hagglund and Kristiina Hayrinen and Ulla-Mari Kinnunen and Pape-Haugaard, \{Louise B.\} and Laura-Maria Peltonen and Kaija Saranto and Philip Scott",

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Cui, W, Cabrera, M & Finkelstein, J 2020, Latent COVID-19 clusters in patients with chronic respiratory conditions. in A Varri, J Delgado, P Gallos, M Hagglund, K Hayrinen, U-M Kinnunen, LB Pape-Haugaard, L-M Peltonen, K Saranto & P Scott (eds), Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators - Proceedings of the EFMI 2020 Special Topic Conference. Studies in Health Technology and Informatics, vol. 275, IOS Press BV, pp. 32-36, EFMI 2020 Special Topic Conference on Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators, Virtual, Online, 26/11/20. https://doi.org/10.3233/SHTI200689

Latent COVID-19 clusters in patients with chronic respiratory conditions. / Cui, Wanting; Cabrera, Manuel; Finkelstein, Joseph.
Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators - Proceedings of the EFMI 2020 Special Topic Conference. ed. / Alpo Varri; Jaime Delgado; Parisis Gallos; Maria Hagglund; Kristiina Hayrinen; Ulla-Mari Kinnunen; Louise B. Pape-Haugaard; Laura-Maria Peltonen; Kaija Saranto; Philip Scott. IOS Press BV, 2020. p. 32-36 (Studies in Health Technology and Informatics; Vol. 275).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Latent COVID-19 clusters in patients with chronic respiratory conditions

AU - Cui, Wanting

AU - Cabrera, Manuel

AU - Finkelstein, Joseph

PY - 2020/11/23

Y1 - 2020/11/23

N2 - The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

AB - The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

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A2 - Varri, Alpo

A2 - Delgado, Jaime

A2 - Gallos, Parisis

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A2 - Hayrinen, Kristiina

A2 - Kinnunen, Ulla-Mari

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A2 - Saranto, Kaija

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PB - IOS Press BV

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Cui W, Cabrera M, Finkelstein J. Latent COVID-19 clusters in patients with chronic respiratory conditions. In Varri A, Delgado J, Gallos P, Hagglund M, Hayrinen K, Kinnunen UM, Pape-Haugaard LB, Peltonen LM, Saranto K, Scott P, editors, Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators - Proceedings of the EFMI 2020 Special Topic Conference. IOS Press BV. 2020. p. 32-36. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI200689

Latent COVID-19 clusters in patients with chronic respiratory conditions

Abstract

Publication series

Conference

Keywords

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