Preference access of users' cancer risk perception using disease-specific online medical inquiry texts

Substantial real cases can be formed in current online medical platforms, constituting potentially rich commercial medical value. In order to obtain the value, it is necessary to mine the preference for user perceived cancer risk in online medical platforms. However, user preference in the platforms varies with medical inquiry text environments, and a user's disease-specific online medical inquiry text environment would also affect his/her behavioral decisions in real time. In this sense, considering the inner relations between different contexts and user preferences under different diseases-specific inquiry text environments and integrating early cancer texts will facilitate the exploration on the law of preference for user perceived cancer risk. Therefore, in this paper, the matrix decomposition and Labeled-LDA models are expanded to propose a context-based method to access the preference for user perceived cancer risk. Firstly, modeling on the relationship between user preferences and information in multi-dimensional context is carried out, and the universal method of integrating multi-dimensional contextual information with user preferences is analyzed. Moreover, more accurate user references were obtained under the multi-dimensional text space and multi-dimensional disease space. Secondly, the similarity relationships between all disease-specific online medical inquiries and early cancer texts are used to obtain user perceived cancer risk, thus knowing the online medical inquiry texts of user cognized diseases and perceiving the cancer risk. Lastly, by accessing the user preferences under different disease topics and user perceived cancer risk in multi-dimensional contexts, the preference for user perceived cancer risk is obtained in a more accurate way. Based on the large-volume real-world dataset, the relationship between each context and user preferences is assessed, and it is concluded that the method proposed in this paper is superior to MF-LDA method in obtaining the preference for user perceived cancer risk. This indicates that the proposed method not only expresses user perceived risk, but also clearly expresses the characteristics of user's preference. Furthermore, it is verified that the integration of context with early cancer text and the establishment of user preference model are feasible and effective.