Publicação

Unveiling precision medicine with Data Mining: Discovering patient subgroups and patterns

Ver documento

Detalhes bibliográficos
Resumo:Data mining techniques, prominently clustering, assume a pivotal role in fortifying precision medicine by facilitating the revelation of patient subgroups that share common attributes. By harnessing clustering for the analysis of data behavior within the realm of precision medicine, distinctive disease patterns, and progression dynamics are unveiled, thereby contributing to the formulation of precisely tailored treatment strategies. This paper aims to present the outcomes derived from a clustering analysis applied to diverse clinical datasets encompassing critical facets such as vital signs, laboratory exams, medications, sepsis, Glasgow Coma Scale, procedures, interventions, diagnostics, and admission/discharge records. This compilation of datasets pertains to a cohort of seventy patients. The resultant analysis uncovers intrinsic patterns and relationships residing within intricate datasets. Executed following the rigorous CRISP-DM methodology, this discovery study identified three distinct clusters that group similar data characteristics, encompassing both categorical and numerical clinical data, and resulted in three major groups: patients with stable health conditions, recovery stage, and at risk. This pivotal outcome catalyzes future endeavors, including classification tasks aimed at identifying new patients within specific classes, thereby advancing the horizons of precision medicine.
Autores principais:Mosavi, Nasimsadat
Outros Autores:Santos, Manuel
Assunto:clinical decision-making clustering data mining intelligence precision medicine
Ano:2023
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

Registos relacionados

Registos relacionados