Hypersphere clustering to characterize healthcare providers using prescriptions and procedures from Medicare claims data.
AMIA Annu Symp Proc
; 2019: 408-417, 2019.
Article
en En
| MEDLINE
| ID: mdl-32308834
ABSTRACT
We consider the task of producing a useful clustering of healthcare providers from their clinical action signature- their drug, procedure, and billing codes. Because high-dimensional sparse count vectors are challenging to cluster, we develop a novel autoencoder framework to address this task. Our solution creates a low-dimensional embedded representation of the high-dimensional space that preserves angular relationships and assigns examples to clusters while optimizing the quality of this clustering. Our method is able to ï¬nd a better clustering than under a two-step alternative, e.g., projected K means/medoids, where a representation is learned and then clustering is applied to the representation. We demonstrate our method's characteristics through quantitative and qualitative analysis of real and simulated data, including in several real-world healthcare case studies. Finally, we develop a tool to enhance exploratory analysis of providers based on their clinical behaviors.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Análisis por Conglomerados
/
Medicare
/
Personal de Salud
Tipo de estudio:
Prognostic_studies
/
Qualitative_research
Límite:
Aged
/
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
AMIA Annu Symp Proc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos