@article{IR,
      recid = {9225},
      author = {Lin, Wei-Chun and Chen, Jimmy S. and Kaluzny, Joel and  Chen, Aiyin and Chiang, Michael F. and Hribar, Michelle R.},
      title = {Extraction of active medications and adherence using  natural language processing for glaucoma patients},
      publisher = {Oregon Health and Science University},
      address = {2021},
      number = {IR},
      abstract = {Accuracy of medication data in EHR is crucial for patient  care and research. Previous work has shown frequent errors  in medication lists include incomplete records, duplicated  prescriptions, and failed discontinuation of medications.  Since medication lists are inaccurate, physicians often  record medication information in progress notes, which is  difficult to automatically extract since notes are written  as free-text narratives. In this study, we developed and  validated a named entity recognition model to identify  current medication and adherence from progress notes. Also,  a prototype tool for medication reconciliation using the  developed model was demonstrated.},
      url = {http://digitalcollections.ohsu.edu/record/9225},
      doi = {https://doi.org/10.6083/q524jp38n},
}