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Structuring and processing natural language is a growing challenge in the medical field. Researchers are looking for new ways to extract knowledge to create databases and applications to help doctors treat patients and minimize medical errors. A very important part in treating a patient is to provide a fair and effective treatment for diseases. In this article we present a method of extracting important information from medical prospectuses, such as a drug-treated condition, a medicine name, a drug type, etc. To extract these entities, we use Stanford NER Tagger trained for prospectuses in Romanian language. The model was trained and tested with 3 types of medication. For each test, the accuracy of the extracted data was calculated. The extracted medical information is used to create databases with structured information that are useful for decision-support applications to check for or find suggestions for the best treatments. |
