Kotoe Katayama

Prescription of Kampo Drugs in the Japanese Health Care Insurance Program

Kampo medicine or traditional Japanese medicine has been used under Japans National Health Insurance scheme for 46 years. Recent research has shown that more than 80% of physicians use Kampo in daily practice. However, the use of Kampo from the patient perspective has received scant attention. To assess the current use of Kampo drugs in the National Health Insurance Program, we analysed a total of 67,113,579 health care claim records, which had been collected by Japans Ministry of Health, Labour and Welfare in 2009. We found that Kampo drugs were prescribed for 1.34% of all patients. Among these, 92.2% simultaneously received biomedical drugs. Shakuyakukanzoto was the most frequently prescribed Kampo drug. The usage of frequently prescribed Kampo drugs differed between the youth and the elderly, males and females, and inpatients and outpatients. Kampo medicine has been employed in a wide variety of conditions, but the prescription rate was highest for disorders associated with pregnancy, childbirth, and the puerperium (4.08%). Although the adoption of Kampo medicine by physicians is large in a variety of diseases, the prescription rate of Kampo drugs is very limited.

Clinical Data Mining Related to the Japanese Kampo Concept “Hie” (Oversensitivity to Coldness) in Men and Pre- and Postmenopausal Women

“Hie” is a subjective oversensitivity to cold and a condition experienced in 60% of Japanese citizens. The condition of hie has not been documented in Western medicine. However, in Kampo medicine, hie is an important target of treatment, because it has been considered one of the sources of all kinds of diseases. This study aimed to clarify the symptoms and findings associated with hie and contribute to increased precision in hie diagnosis. During 2005-2006, data from interviews of 1691 patients during their initial visit to the Kampo Clinic of Keio University Hospital were analyzed using a classification and regression tree (CART) analysis, a data mining technique. Symptoms and findings characteristic of each group are follows as, postmenopausal women: fatigability, absence of lower abdominal pain, and absence of hot flashes of feet: women with menstruation: leg swelling, knee pain, and abdominal pain; men: insomnia, leg weakness, and absence of excess saliva. From the perspective of Kampo medicine the result suggested that the feature of hie condition in postmenopausal women, women with menstruation, and men is statistically different.

The Difference between the Two Representative Kampo Formulas for Treating Dysmenorrhea: An Observational Study

In Kampo medicine, two different formulas are effective for treating dysmenorrhea—tokishakuyakusan and keishibukuryogan; however, the criteria by which specialists select the appropriate formula for each patient are not clear. We compared patients treated with tokishakuyakusan and those with keishibukuryogan and proposed a predictive model. The study included 168 primary and secondary dysmenorrhea patients who visited the Kampo Clinic at Keio University Hospital. We collected clinical data from 128 dysmenorrhea patients, compared the two patient groups and selected significantly different factors as potential predictors, and used logistic regression to establish a model. An external validation was performed using 40 dysmenorrhea patients. Lightheadedness, BMI < 18.5, and a weak abdomen were significantly more frequent in the tokishakuyakusan group; tendency to sweat, heat intolerance, leg numbness, a cold sensation in the lower back, a strong abdomen, and paraumbilical tenderness and resistance were more frequent in the keishibukuryogan group. The final model fitted the data well. Internally estimated accuracy was 81.2%, and a leave-one-out cross-validation estimate of accuracy was 80.5%. External validation accuracy was 85.0%. We proposed a model for predicting the use of two Kampo formulas for dysmenorrhea, which should be validated in prospective trials.

Clustering for visual analogue scale data in symbolic data analysis

Abstract We propose a hierarchical clustering for the visual analogue scale (VAS) in the framework of Symbolic Data Analysis(SDA). The VAS is a method that can be readily understood by most people to measure a characteristic or attitude that cannot be directly measured. VAS is of most value when looking at change within people, and is of less value for comparing across a group of people because they have different sense. It could be argued that a VAS is trying to produce interval/ratio data out of subjective values that are at best ordinal. Thus, some caution is required in handling VAS. We describe VAS as distribution and handle it as new type data in SDA. In this paper, we define “VAS distribution” as new type data in SDA and propose a hierarchical clustering for this new type data.

Symbolic Hierarchical Clustering for Pain Vector

We propose a hierarchical clustering in the framework of Symbolic Data Analysis(SDA). SDA was proposed by Diday at the end of the 1980s and is a new approach for analysing huge and complex data. In SDA, an observation is described by not only numerical values but also “higher-level units”; sets, intervals, distributions, etc. Most SDA works have dealt with only intervals as the descriptions. We already proposed “pain distribution” as new type data in SDA. In this paper, we define new “pain vector” as new type data in SDA and propose a hierarchical clustering for this new type data.

Symbolic hierarchical clustering for visual analogue scale data

We propose a hierarchical clustering in the framework of Symbolic Data Analysis(SDA). SDA was proposed by Diday at the end of the 1980s and is a new approach for analysing huge and complex data. In SDA, an observation is described by not only numerical values but also “higher-level units”; sets, intervals, distributions, etc. Most SDA works have dealt with only intervals as the descriptions. In this paper, we define “pain distribution” as new type data in SDA and propose a hierarchical clustering for this new type data.