A prototype medical decision support system that utilizes 'sensitivity' and 'specificity' of a clinical feature for diagnosis. a novel approach

Abstract

A prototype computer based secision support system was developed to simulate a doctors' decision making process using a relational database consisting 25 clinical features and 10 common diseases encountered in critical care. The relationship between diseases and clinical features was cited by a sensitivity and a specificity value for each clinical expert arbitrarily determined the sensitivity and specificity values. The cumulative probability values of each disease in relation to presenting clinical features were calculated using simple decision algorithm with ranked values to determine the most probable diagnosis. THe database was built using microsoft Access and the interfaced in Visual Basic environment. IN the program the output window provides the user with 5 most likely diagnoses with a display of ranked probability values. This differential diagnosis can be refined repetitively using new information. THe system was validated using data from 26 patients admitted to a regional intensive care unit. The prototype decision support system was able to predict the true diagnosis with a sensitivity value of 88% as rank 1 an 96% as both rank 1 or 2. Thus results show that this novel approach of decision support could be more reliable t assist a doctor.