A Novel CMAUT-UML Framework for the Optimisation of Clinical Information System (CIS) and Prediction of CVD Percentage Risk

This research critically analyses the different types of clinical data representation used in modelling Clinical Information Systems (CIS) and their limitations. It identifies space complexity, information overload, performance degradation, erroneous data retrieval and transmission as some of the main challenges caused by inappropriate data representation. Literature reviewed, indicated that object-oriented Health Level 7 (HL7), Entity Attribute Value (EAV), Advanced ERD with XML, and ERD –FOL (First Order Logic) are some of the contemporary methods used in modelling and optimising CIS. However, these approaches do not address the space complexity and information overload issues because of the multi-dimensional, complex large-scale nature of clinical datasets. Therefore, this research proposes a unique framework that uses object-oriented (UML) technique and combinatorial multiple attribute utility theory (CMAUT) as a new clinical data re-representation. In the CMAUT framework, the human organs, their multiple attributes and relationships are modelled using classes. The attributes of each organ class are written as logical expressions using CMAUT concepts, which are linked to each other with logical connectors AND for complementary organs such as cardiovascular and OR for substitutable organs like kidneys.
The logical expressions are converted into mathematical format, which serves as the utility objective function that is optimised using linear programming method subject to a set of constraint matrix. The constraint matrix is generated by transforming the multiple attributes in the CMAUT expressions into algebraic expressions by applying an algorithm that uses unit matrix and Raman transformation table. The output of the framework gives a set of attribute values, which optimal value maximises the overall utility of the objective function in the combinatorial organs. The algorithm maps the resultant attribute values to the appropriate attributes of the organs to determine the optimal amount of data required to be retrieved for primary health care investigation. The framework retrieves and transmits only needed data for investigation thus reducing the information overload and space complexity in the CIS.
The framework was implemented using the MATLAB software and validated with clinical data from the cardiovascular disease survey in England report. Functionality test conducted, revealed that for complementary organs the space complexity is θ (n + 1) using the framework and θ (2n) without the framework. Substitutable organs gave an exponential expansion of θ (2n) in both cases. Simulation conducted showed that the mean size of the data retrieved for investigation using the framework is 463.5 bytes as compared to 1216.6 bytes without it. Statistical tests carried out using the output data from the framework gave a p-value of 0.000. Hence the hypothesis that the amount of data required for primary care health investigation can be reduced when the clinical data is re-represented with UML/CMAUT and optimised using LP based algorithm is statistically significant. For hypertension disease, by converting the optimal values from the framework into percentages give results similar to the percentage risk of the user been hypertensive. The output values were benchmarked against Framingham web based heart risk calculators and statistically analysed. Hence, the novelty of the framework is that it can be used for optimising CIS, as a multi-attribute decision tool and as an epidemiological prediction model for detecting high blood pressure diseases.