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Not only the side effects of medicines themselves, but also their abuse, namely the lack of safety in drug usage, can cause serious medical accidents. The latter applies to the case of the mix-up of medicines, double dose or insufficient dose. Medical equipments can also cause accidents because of wrong treatment, such as wrong input to equipments and wrong power-off. In order to avoid such accidents, it is necessary to investigate past cases to identify their causes and work out counter measures. Medical near-miss cases caused by wrong treatment with the medicines or the medical equipments are strongly related to medical accidents that occur due to the lack in safety of usage. Medical near-miss cases are incidents, which could be medical accidentsavoided owing to certain factors, and happen more frequently than medical accidents. Incorporating Heinrich’s law, which shows the tendency of frequency and seriousness of industrial accidents, we estimate that near-miss cases happen three hundred times per one serious medical accident or thirty minor accidents. This can be interpreted as there being many causes of medical accidents, most of which are eliminated by certain suppression factors, which lead to near-miss cases. The rest of the causes lead to medical accidents. From this perspective, we can expect that both medical accidents and near-miss cases originate from the same type of causes, which suggests that the analysis of data on near-miss cases is valid to investigate the cause of medical accidents, since their occurrence frequency is much larger than that of medical accidents. For the reasons stated above, we analyze the data of medical near-miss cases related to drugs and medical equipments, which have been collected in previous years to determine the root cause of medical accidents caused by the neglect of safety of usage. Though simple aggregation calculations and descriptive statistics have already been applied to them, the analyses are too simple to extract sufficient information such as pairs of medicines that tend to be confused, and the relationships between the contents of incidents and the causes. To realize such analyses, we utilize data mining techniques such as decision-tree and market-basket analysis, and text-mining techniques such as the word linking method. The related works analyzing medical databy utilizing natural language processing ormachine learningwere introduced by Hripcsak et al (Hripcsak et al., 2003), who suggested the framework todetect events such as medical errors or adverse outcome.Recently,

Application of Data Mining and Text Mining to the Analysis of Medical near Miss Cases