Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F24%3A10158810" target="_blank" >RIV/00098892:_____/24:10158810 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s13755-024-00308-4" target="_blank" >https://link.springer.com/article/10.1007/s13755-024-00308-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13755-024-00308-4" target="_blank" >10.1007/s13755-024-00308-4</a>

Result language

angličtina

Original language name

Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients

Original language description

Purpose: Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Methods: Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. Results: 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Conclusion: Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Health Information Science and Systems

ISSN

2047-2501

e-ISSN

2047-2501

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

50

UT code for WoS article

001339867100001

EID of the result in the Scopus database

2-s2.0-85207525359