Design and Development of a Methodology Based on Expert Systems, Applied to the Treatment of Pressure Ulcers

Abstract

The medical treatment of chronic wounds, pressure ulcers in particular, burdens healthcare systems nowadays with high expenses that result mainly from their monitoring and assessment stages. Decision support systems applied within the ‘remote medicine’ framework may be of help, not only to the process of monitoring the evolution of chronic wounds under treatment, but also to facilitate the prevention and early detection of potential risk conditions in the affected patients. In this paper, the design and definition of a new decision-support methodology to be applied to the monitoring and assessment stages of the medical treatment process for pressure ulcers is proposed. Built upon the use and development of expert systems, the methodology makes it possible to generate alerts derived from the evolution of a patient’s chronic wound, by means of the interpretation and combination of data coming from both an image of the wound, and the considerations of a healthcare professional with expertise in the subject matter. Some positive results are already shown regarding the determination of the ulcer’s status in the tests that have been carried out so far. Therefore, it is considered that the proposed methodology might lead to substantial improvements regarding both the treatment’s efficiency and cost savings.

Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks

“…The methodology is based on the use of two concurrent expert systems [ 96 , 97 , 98 ] for the calculation of the radon risk value, together with a decision tree-based regression model, that will determine the final recommendations associated to such risk. All of them are respectively labelled in Figure 1 as ‘Fuzzy inference system Fc’, ‘Fuzzy inference system RR’ and ‘Regression Tree #Number of Tree’.…”

“…The representation of antecedents and consequent is carried out by means of the well-known membership functions—the mathematical representation of a fuzzy set—that determine the degree of membership of a certain value to a particular set. The subsequent combination and aggregation of antecedents and consequents uses different specific operators that ensure the graphical combination of the membership functions [ 96 , 98 , 102 ].…”

“…After a relevant number of data collection cycles, it is possible to establish the correlation coefficient that exists between the radon concentration level and the different environmental and atmospheric variables, from the values stored into the database. After that, processing is carried out on the data collected by the two Mamdani-type fuzzy-logic expert systems that work in a concurrent way [ 96 , 97 , 98 ]. The first system is provided with a feedback from the historian as Figure 1 shows, in which depending on the correlation existing between the radon concentration level and the value of the different atmospheric variables, the values of the different rules of the system itself are weighted in one or another way.…”

“…After the aggregation of the consequents, the final value of the correction factor value is calculated by means of the defuzzification process based on the centroid method [ 102 ]. In the work by Casal-Guisande et al [ 96 ] a more detailed explanation of the development of these systems is provided.…”

“…So far the correction factor and the radon risk have been calculated, obtained after the defuzzification of their corresponding inference systems, which work concurrently [ 96 , 97 , 98 ]. Such information must be structured into a knowledge base, together with the values of the atmospheric variables, the radon concentration, and the level of each one of the corrective measures associated to each dataset (variables and concentration values).…”