Cargando…
Learning relevance models for patient cohort retrieval
OBJECTIVE: We explored how judgements provided by physicians can be used to learn relevance models that enhance the quality of patient cohorts retrieved from Electronic Health Records (EHRs) collections. METHODS: A very large number of features were extracted from patient cohort descriptions as well...
Autores principales: | Goodwin, Travis R, Harabagiu, Sanda M |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6241510/ https://www.ncbi.nlm.nih.gov/pubmed/30474078 http://dx.doi.org/10.1093/jamiaopen/ooy010 |
Ejemplares similares
-
Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification
por: Maldonado, Ramon, et al.
Publicado: (2017) -
Deep Learning from EEG Reports for Inferring Underspecified Information
por: Goodwin, Travis R., et al.
Publicado: (2017) -
Memory-Augmented Active Deep Learning for Identifying Relations Between Distant Medical Concepts in Electroencephalography Reports
por: Maldonado, Ramon, et al.
Publicado: (2018) -
A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records
por: Goodwin, Travis, et al.
Publicado: (2015) -
Inferring the Interactions of Risk Factors from EHRs
por: Goodwin, Travis, et al.
Publicado: (2016)