Multimodal based clouds computing systems for healthcare and risk forecasting based on subjective analysis

Abstract

In decision making most approaches are taking into account objective criteria, however the subjective correlation among decision makers provided as preference utility is necessary to be presented to provide confidence preference additive among decision makers reducing ambiguity and produce better utility preferences measurement for subjective criteria among decision makers. Most models in Decision support systems are assuming criteria as independent. Therefore, these models are ranking alternatives based on objective data analysis. Also, different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to do decision making using classical multi criteria decision making models. Sophisticated machine learning methods to estimate or extract emotions from the content created by users has been developed including support vector machines, Bayesian networks, maximum entropy approaches and concept level analysis of natural language text, supported by combinations of common-sense reasoning. These approaches are mainly based on language text processing with sufficient documents, which is usually inlarge is not available. We think Subjectiveness is related to the contextual form of criteria. Uncertainty of some criteria in decision making is also considered as other important aspect These draw backs in decision making are major research challenges that are attracting wide attention, like on big data analysis for risk prediction, medical diagnosis and other applications that are in practice more subjective to user situation and its knowledge related context. Subjectivity would be examined based on correlations between different contextual structures that is reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations. The Virtual Doctor System (VDS) developed by my group is a system assisting human doctor who is practicing medical diagnosis in real situation and environment. The interoperability is represented by utilizing the medical diagnosis cases of medical doctor, represented in machine executable fashion based on human patient interaction with virtual avatar resembling a real doctor. VDS is practiced as a virtual avatar interacting with the human patient based on physical views and mental view analysis. In this talk I outline our VDS system and then discuss related issues in subjective decision making in medical domain. Using fuzzy reasoning techniques in VDS, it has been shown that it is possible to provide better precision in circumstances that is related to partial known data and uncertainty on the acquisition of medical symptoms.