Predicting Japanese Kampo formulas by analyzing database of medical records: a preliminary observational study.

BACKGROUND: Approximately 90 % of physicians in Japan use Kampo medicine in daily practice. However, it is a challenge for physicians who do not specialize in Kampo medicine to select a proper Kampo formula out of the 148 officially approved formulas, as the decision relies on traditional measurements and traditional medicine pattern diagnoses. The present study tries to evaluate the feasibility of a decision support system for frequently used Kampo formulas. METHODS: Our study included 393 patients who visited the Kampo Clinic at Keio University Hospital for the first time between May 2008 and March 2013. We collected medical records through a browser-based questionnaire system and applied random forests to predict commonly prescribed Kampo formulas. RESULTS: The discriminant rate was the highest (87.0 %) when we tried to predict a Kampo formula from two candidates using age, sex, body mass index, subjective symptoms, and the two essential and predictable traditional medicine pattern diagnoses (excess-deficiency and heat-cold) as predictor variables. The discriminant rate decreased as the candidate Kampo formulas increased, with the greatest drop occurring between three (76.7 %) and four (47.5 %) candidates. Age, body mass index, and traditional medicine pattern diagnoses had higher importance according to the characteristics of each Kampo formula when we utilized the prediction model, which predicted a Kampo formula from among three candidates. CONCLUSIONS: These results suggest that our decision support system for non-specialist physicians works well in selecting appropriate Kampo formulas from among two or three candidates. Additional studies are required to integrate the present statistical analysis in clinical practice.

Discrimination of prediction models between cold-heat and deficiency-excess patterns.

OBJECTIVE: The purpose of this study was to extract important patient questionnaire items by creating random forest models for predicting pattern diagnosis considering an interaction between deficiency-excess and cold-heat patterns. DESIGN: A multi-centre prospective observational study. SETTING: Participants visiting six Kampo speciality clinics in Japan from 2012 to 2015. MAIN OUTCOME MEASURE: Deficiency-excess pattern diagnosis made by board-certified Kampo experts. METHODS: We used 153 items as independent variables including, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We sampled training data with an equal number of the different patterns from a 2 × 2 factorial combination of deficiency-excess and cold-heat patterns. We constructed the prediction models of deficiency-excess and cold-heat patterns using the random forest algorithm, extracted the top 10 essential items, and calculated the discriminant ratio using this prediction model. RESULTS: BMI and blood pressure, and subjective symptoms of cold or heat sensations were the most important items in the prediction models of deficiency-excess pattern and of cold-heat patterns, respectively. The discriminant ratio was not inferior compared with the result ignoring the interaction between the diagnoses. CONCLUSIONS: We revised deficiency-excess and cold-heat pattern prediction models, based on balanced training sample data obtained from six Kampo speciality clinics in Japan. The revised important items for diagnosing a deficiency-excess pattern and cold-heat pattern were compatible with the definition in the 11th version of international classification of diseases.

Classification of patients with cold sensation by a review of systems database: A single-centre observational study.

OBJECTIVES: In Kampo medicine, a traditional medicine pattern(TM1) refers to the complete clinical presentation of the patient at a given moment in time. Candidate herbal formulas are chosen for a chief complaint, and an appropriate formula is determined on the basis of the pattern(TM1) diagnosis. In this study, we demonstrated the importance of accompanying symptoms in diagnosing traditional medicine patterns(TM1). DESIGN: Single centre observational study. SETTING: We analysed data from 524 new patients with a hypersensitivity to cold sensation as their primary diagnosis (mean age 51.6 ± 17.8 years; female ratio 82.1%) who visited the Keio University Hospital Kampo Clinic between 2008 and 2013. MAIN OUTCOME MEASURES: Accompanying symptoms were recorded on the browser-based e-questionnaire system, which contained 128 items. The Japan Society for Oriental Medicine's board certified Kampo specialists diagnosed the traditional medicine patterns(TM1). RESULTS: When participants were classified according to the origin of their cold sensation, there were no differences in their traditional medicine patterns. In contrast, when patients were classified based on the number of accompanying symptoms, a significant difference in the patterns was identified. An increasing number of accompanying symptoms was associated with more frequent qi stagnation and blood stasis pattern(TM1). Patients with a qi stagnation pattern had higher rates of depression and insomnia. In contrast, patients with a blood stasis pattern(TM1), had higher rates of acne, body stiffness, and menstrual abnormality. CONCLUSIONS: Qi stagnation and blood stasis patterns(TM1) are related to a number of different accompanying symptoms in the patients with hypersensitivity to cold sensation.

Prediction of deficiency-excess pattern in Japanese Kampo medicine: Multi-centre data collection.

OBJECTIVE: The purpose of the present study was to compare important patient questionnaire items by creating a random forest model for predicting deficiency-excess pattern diagnosis in six Kampo specialty clinics. DESIGN: A multi-centre prospective observational study. SETTING: Participants who visited six Kampo specialty clinics in Japan from 2012 to 2015. MAIN OUTCOME MEASURE: Deficiency-excess pattern diagnosis made by board-certified Kampo experts. METHODS: To predict the deficiency-excess pattern diagnosis by Kampo experts, we used 153 items as independent variables, namely, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We extracted the 30 most important items in each clinic's random forest model and selected items that were common among the clinics. We integrated participating clinics' data to construct a prediction model in the same manner. We calculated the discriminant ratio using this prediction model for the total six clinics' data and each clinic's independent data. RESULTS: Fifteen items were commonly listed in top 30 items in each random forest model. The discriminant ratio of the total six clinics' data was 82.3%; moreover, with the exception of one clinic, the independent discriminant ratio of each clinic was approximately 80% each. CONCLUSIONS: We identified common important items in diagnosing a deficiency-excess pattern among six Japanese Kampo clinics. We constructed the integrated prediction model of deficiency-excess pattern.

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.

Terminology-driven literature mining and knowledge acquisition in biomedicine.

In this paper we describe Tagged Information Management System (TIMS), an integrated knowledge management system for the domain of molecular biology and biomedicine, in which terminology-driven literature mining, knowledge acquisition (KA), knowledge integration (KI), and XML-based knowledge retrieval are combined using tag information management and ontology inference. The system integrates automatic terminology acquisition, term variation management, hierarchical term clustering, tag-based information extraction (IE), and ontology-based query expansion. TIMS supports introducing and combining different types of tags (linguistic and domain-specific, manual and automatic). Tag-based interval operations and a query language are introduced in order to facilitate KA and retrieval from XML documents. Through KA examples, we illustrate the way in which literature mining techniques can be utilised for knowledge discovery from documents.