{"title":"New method measuring effect of medical consultation recommendation","authors":"S. Sakurai, Rumi Hayakawa, Hideki Iwasaki","doi":"10.1109/ISMICT.2015.7107517","DOIUrl":null,"url":null,"abstract":"This paper proposes a new method that quantatively measures the effect of medical consultation recommendation. It uses a new transition probability model. The model is composed of 7 kinds of status related to a specific disease. The status is “no consultation”, “consultation”, “slight”, “moderate”, “serious”, “healthy”, and “secession”. The method estimates the transition probability between status by analyzing both the medical examination data and the medical receipt data. It evaluates reactions of object members which are candidate participating in the program of medical consultation recommendation. The transition probability is adjusted by the reactions. This paper focuses on the diabetes as the target disease and applies the method to the data managed by Toshiba health insurance union. Lastly, it verifies the effect of the method based on the simulated result.","PeriodicalId":6624,"journal":{"name":"2015 9th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"8 1","pages":"148-152"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 9th International Symposium on Medical Information and Communication Technology (ISMICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMICT.2015.7107517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes a new method that quantatively measures the effect of medical consultation recommendation. It uses a new transition probability model. The model is composed of 7 kinds of status related to a specific disease. The status is “no consultation”, “consultation”, “slight”, “moderate”, “serious”, “healthy”, and “secession”. The method estimates the transition probability between status by analyzing both the medical examination data and the medical receipt data. It evaluates reactions of object members which are candidate participating in the program of medical consultation recommendation. The transition probability is adjusted by the reactions. This paper focuses on the diabetes as the target disease and applies the method to the data managed by Toshiba health insurance union. Lastly, it verifies the effect of the method based on the simulated result.