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Recovery Medication from Free Text to a Structured Form.
Humberto Fernán Mandirola Brieux1, Santiago Orozco1, Marisa Lanfranconi1
1Sanatorio Otamendi, Buenos Aires, Argentina.
Studies in Health Technology and Informatics
|August 24, 2019
Summary
We compared Natural Language Processing (NLP) and Parsing Process (PP) for structuring Spanish medical prescriptions. Parsing Process slightly outperformed NLP, but further research is needed to combine methods for improved prescription data extraction.
Area of Science:
- Health Informatics
- Computational Linguistics
- Clinical Pharmacy
Background:
- Converting free-text medical prescriptions into structured data is crucial for pharmacy operations and nursing care planning.
- Existing methods require evaluation for efficiency and accuracy in handling clinical documentation.
Purpose of the Study:
- To compare the effectiveness of Natural Language Processing (NLP) and Parsing Process (PP) in structuring Spanish medical prescriptions.
- To assess the performance of these methods in extracting actionable information for healthcare providers.
Main Methods:
- Analysis of 87,750 Spanish medical prescriptions, with 65,000 processed.
- Comparative evaluation of NLP and PP techniques for converting unstructured prescription text into a structured format.
Main Results:
- Parsing Process (PP) achieved a 65% recovery rate, slightly higher than Natural Language Processing (NLP) at 62%.
- The observed difference in performance between NLP and PP was statistically significant (p < 0.001).
Conclusions:
- Both NLP and PP demonstrate utility in structuring medical prescriptions, with PP showing a marginal advantage.
- Further investigation into combining NLP and PP methodologies is warranted to potentially enhance data extraction performance.
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