Prescription extraction using CRFs and word embeddings

- A high-performing system to extract and organize prescription information.
- Evaluating the contribution of real-valued word embeddings in clinical NER.
- Tackling medication relation extraction as a sequence labeling task using CRFs.

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.

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