Medical Recommendation in Python

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.