Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events.
Proc Int Conf Mach Learn
; 2012: 1287-1294, 2012.
Article
en En
| MEDLINE
| ID: mdl-25285329
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Patient_preference
Idioma:
En
Revista:
Proc Int Conf Mach Learn
Año:
2012
Tipo del documento:
Article
País de afiliación:
Bélgica
Pais de publicación:
Estados Unidos