Keita Nabeta

Analysis on descriptions of precautionary statements in package inserts of medicines

Background To prevent medical accidents, users must be informed of the cautions written in medical package inserts. To realize countermeasures by utilizing information systems, we must also implement a drug information database. However, this is not easy to develop, since the descriptions in package inserts are too complex and their information poorly structured. It is necessary to analyze package insert information and propose a data structure. Methods We analyzed the descriptions of ‘precautions for application’ in package inserts via text mining methods. In order to summarize statements, we applied dependency analysis to statements and visualized their relations between predicate words and other words. Furthermore, we extracted words representing timing to execute the order. Results We found that there are four types of statements: direct orders such as “ ” (use), causative orders such as “ ” (make someone use), direct interdictions such as “ ” (do not use), and causative interdictions such as “ ” (do not make user use). As for words representing timing, we extracted six groups: ”at the time of delivery,” “at the time of preparation,” “in use,” “after use,” and “at the time of storage.” From these results, we obtained points of consideration concerning the subjects of orders in the statements and timing of their execution. Conclusion From the obtained knowledge, we can define the information structure used to describe the precautionary statement. It should contain information such as the actions described in the statement, the flag to express an order or interdiction, the subject to be ordered, and the timing.

The similarity index of character shape of medicine names based on character shape similarity (II)

The similarity of drug names in Japanese such as ??? (Amaryl) and ??? (Almarl) causes confusion over drug names and can lead to medical errors. In order to prevent such errors, methods of computing their similarity have been proposed. However, there are no studies that take account of character shape similarity quantitatively. In a previous study, we calculated the character shape similarity by template matching technique and proposed a method of measuring medicine name similarity based on it. Although we obtained a high correlation coefficient between the medicine name similarity values and subjective evaluation, we observed some character pairs that are not similar. In this study, we improved the method of computing the character shape similarity based on the characteristic points of character and compared it with advanced methods.

Appearance similarity index for medicinal ampoule labels

Since there are many ampoule injection medicines, it is important to make their labels easily distinguishable because confusing labels may lead to fatal accidents caused by administering the wrong medicine by mistake. In this paper, we utilize Fourier series expansion and wavelet transformation to extract the characteristics in labels and propose an index to measure similarity that we feel toward ampoule labels to prevent confusion in label designs. We also discuss a way of parameterizing colors.

A proposal of contraindication database for medicines

In recent years, despite various measures taken to reduce medical accidents as a result of confusions over drugs, cases of medical malpractice have occurred in Japan. As a countermeasure supported by a Health Labor Sciences Research Grant in 2009, drug information databases based on drug package inserts have been created for computer systems to prevent accidents caused by incorrect treatment of drug information [1]. However, the data in the databases remains problematic. In this study, we propose data item sets to be defined in drug information databases.