Preprocessing Algorithms of Fuzzy Medical Data of Patient Examinations

Abstract

This research work proposes a novel algorithms of processing fuzzy data got while medical examinations of patients in the hospital. These algorithms are based on the use of a set of binary masks of a set of examination parameter states, classified by parameter sets for each type of medical examination. The amount of medical examinations for patient in a hospital differs by a more comprehensive set of examinations. And the amount of fuzzy data generated is significantly higher than in outpatient diagnostics. To solve problems associated with getting a clinical diagnosis, predicting the dynamics of a patient's clinical condition and the outcome of a patient's treatment based on medical screenings and examinations the specific algorithms are necessary both at the initial stage and during treatment. These algorithms are coding / decoding, classification and building of structured interconnected models and states of the diagnostic system throughout the entire patient treatment process. A diagnostic system is a set of methods, knowledge and algorithms for solving the problem of decision support in the clinical diagnostics of patients' diseases in a hospital or outpatient clinic. The methods and algorithms presented in the article allow pre-processing fuzzy data of medical examinations of patients in order to optimize and further use fuzzy cognitive models and artificial intelligence methods. Also a method for the classification of medical examination parameters is proposed for applying the proposed algorithms.