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Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.

Wang Y, Steinhubl SR, Defilippi C, Ng K, Ebadollahi S, Stewart WF, Byrd RJ - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR.PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems.To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

View Article: PubMed Central - PubMed

Affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.

ABSTRACT
Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR. PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems. To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

No MeSH data available.


Examples of candidate annotations and their components.
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f2-2090064: Examples of candidate annotations and their components.

Mentions: We created parsing rules to recognize candidate MedDescriptions (for MED_REC) and Prescriptions (for encounter notes). The top-level rules, which are 1CA aggregate rules (i.e., they operate over an entire sentence, skipping extraneous tokens) are: Med Description ➔ MedPart? MedName MedPart{l,4} and Prescription ➔ RxPart* MedName RxPart*. MedPart can he any of MedStrength, MedRoute. MedForm, or MedSize. RxPart can be any of MedDosage, MedFrequency, MedDuration, PrescriptionVerb, or a MedPart. Example candidates are shown in Figure 2.


Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.

Wang Y, Steinhubl SR, Defilippi C, Ng K, Ebadollahi S, Stewart WF, Byrd RJ - AMIA Jt Summits Transl Sci Proc (2015)

Examples of candidate annotations and their components.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525262&req=5

f2-2090064: Examples of candidate annotations and their components.
Mentions: We created parsing rules to recognize candidate MedDescriptions (for MED_REC) and Prescriptions (for encounter notes). The top-level rules, which are 1CA aggregate rules (i.e., they operate over an entire sentence, skipping extraneous tokens) are: Med Description ➔ MedPart? MedName MedPart{l,4} and Prescription ➔ RxPart* MedName RxPart*. MedPart can he any of MedStrength, MedRoute. MedForm, or MedSize. RxPart can be any of MedDosage, MedFrequency, MedDuration, PrescriptionVerb, or a MedPart. Example candidates are shown in Figure 2.

Bottom Line: Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR.PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems.To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

View Article: PubMed Central - PubMed

Affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.

ABSTRACT
Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR. PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems. To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

No MeSH data available.