Cargando…
Toward Complete Structured Information Extraction from Radiology Reports Using Machine Learning
Unstructured and semi-structured radiology reports represent an underutilized trove of information for machine learning (ML)-based clinical informatics applications, including abnormality tracking systems, research cohort identification, point-of-care summarization, semi-automated report writing, an...
Autores principales: | Steinkamp, Jackson M., Chambers, Charles, Lalevic, Darco, Zafar, Hanna M., Cook, Tessa S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646440/ https://www.ncbi.nlm.nih.gov/pubmed/31218554 http://dx.doi.org/10.1007/s10278-019-00234-y |
Ejemplares similares
-
An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images
por: Zadeh, Firoozeh Abolhasani, et al.
Publicado: (2022) -
Radiological diagnosis of the inner ear malformations in children with sensorineural hearing loss
por: Quirk, Bernadine, et al.
Publicado: (2019) -
Towards Social Radiology as an Information Infrastructure: Reconciling the Local With the Global
por: Motta, Gustavo Henrique Matos Bezerra
Publicado: (2014) -
Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma
por: Wang, Dingqian, et al.
Publicado: (2021) -
Tumor information extraction in radiology reports for hepatocellular
carcinoma patients
por: Yim, Wen-wai, et al.
Publicado: (2016)