Work

Prescription extraction using CRFs and word embeddings

Year: 2017

Type: article

Abstract: In medical practices, doctors detail patients' care plan via discharge summaries written in the form of unstructured free texts, which among the others contain medication names and prescription information. Extracting prescriptions from discharge summaries is challenging due to the way these documents are written. Handwritten rules and medical gazetteers have proven to be useful for this purpose but come with limitations on performance, scalability, and generalizability. We instead present a machine learning approach to extract and organize medication names and prescription information into individual entries. Our approach utilizes word embeddings and tackles the task in two extraction steps, both of which are treated as sequence labeling problems. When evaluated on the 2009 i2b2 Challenge official benchmark set, the proposed approach achieves a horizontal phrase-level F1-measure of 0.864, which to the best of our knowledge represents an improvement over the current state-of-the-art.

Source: Journal of Biomedical Informatics

DOI: 10.1016/j.jbi.2017.07.002

Authors: Carson Tao, Michele Filannino, Özlem Uzuner

Institutions: State University of New York, University at Albany, State University of New York

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,410

Funders: University of Sydney, National Institutes of Health, Engineering and Physical Sciences Research Council, National Institute of Mental Health