Visual cluster analysis in support of clinical decision intelligence.
AMIA Annu Symp Proc
; 2011: 481-90, 2011.
Article
em En
| MEDLINE
| ID: mdl-22195102
ABSTRACT
Electronic health records (EHRs) contain a wealth of information about patients. In addition to providing efficient and accurate records for individual patients, large databases of EHRs contain valuable information about overall patient populations. While statistical insights describing an overall population are beneficial, they are often not specific enough to use as the basis for individualized patient-centric decisions. To address this challenge, we describe an approach based on patient similarity which analyzes an EHR database to extract a cohort of patient records most similar to a specific target patient. Clusters of similar patients are then visualized to allow interactive visual refinement by human experts. Statistics are then extracted from the refined patient clusters and displayed to users. The statistical insights taken from these refined clusters provide personalized guidance for complex decisions. This paper focuses on the cluster refinement stage where an expert user must interactively (a) judge the quality and contents of automatically generated similar patient clusters, and (b) refine the clusters based on his/her expertise. We describe the DICON visualization tool which allows users to interactively view and refine multidimensional similar patient clusters. We also present results from a preliminary evaluation where two medical doctors provided feedback on our approach.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Gráficos por Computador
/
Interface Usuário-Computador
/
Análise por Conglomerados
/
Sistemas de Apoio a Decisões Clínicas
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2011
Tipo de documento:
Article