Using the natural language processing system MedNER-J to analyze pharmaceutical care records: Natural language processing analysis (Preprint)

Abstract

Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians’ records, it has yet to be widely applied to pharmaceutical care records.

In this report, we investigated the feasibility of automatic extraction of patients’ diseases and symptoms from pharmaceutical care records. The verification was performed using MedNER-J, a Japanese disease-extraction system designed for physicians’ records.

MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F-measure.

The F-measure of NER for subjective, objective, assessment, and plan data was 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive–negative classification, the F-measure was 0.28, 0.39, 0.64, and 0.077, respectively. The F-measure of NER for objective and assessment data (F=0.70, 0.76) was higher than that for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F-measure of NER and positive-negative classification was high for assessment data alone (F=0.64), which was attributed to the similarity of its description format and contents to those of the training data.

MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.