CLINICAL HETEROGENEITY IN THE AGE OF BIG DATA, ADVANCED ANALYTICS, AND COMPLEXITY THEORY.
Trans Am Clin Climatol Assoc
; 133: 56-68, 2023.
Article
em En
| MEDLINE
| ID: mdl-37701617
ABSTRACT
Clinical heterogeneity remains a challenge in the practice of medicine and is an underlying motivation for much of biomedical research. Unfortunately, despite an abundance of technologies capable of producing millions of discrete data elements with information about a patient's health status or disease prognosis, our ability to translate those data into meaningful improvements in understanding of clinical heterogeneity is limited. To address this gap, we have applied newer approaches to manifold learning and developed additional and complementary techniques to interrogate and interpret complex, high dimensional omics data. The central premise is that there exist manifolds embedded in high dimensional data that represent fundamental biologic processes that may help address the challenges of clinical heterogeneity. Preliminary evidence from several real-world data sets suggests that these techniques can identify coherent and reproducible manifolds embedded in higher dimensional omics data. Work is currently ongoing to determine the clinical informativeness of these novel data structures.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pesquisa Biomédica
/
Medicina
Aspecto:
Patient_preference
Limite:
Humans
Idioma:
En
Revista:
Trans Am Clin Climatol Assoc
Ano de publicação:
2023
Tipo de documento:
Article