A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.
Artif Intell Med
; 148: 102750, 2024 02.
Article
en En
| MEDLINE
| ID: mdl-38325922
ABSTRACT
Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Enfermedad Crítica
/
COVID-19
Tipo de estudio:
Observational_studies
/
Prevalence_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Artif Intell Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article