FORMALIZING THE STAGES OF MAMMOGRAPHIC EXAMINATIONS IN DESIGNING A MEDICAL DECISION SUPPORT SYSTEM

2020 ◽  
Vol 3 (4) ◽  
pp. 279-291
Author(s):  
Anatoly I. Povoroznyuk ◽  
Khaled Shehna ◽  
Oksana A. Povoroznyuk
Keyword(s):  
Decision Support ◽  
Morphological Analysis ◽  
Functional Model ◽  
Medical Decision ◽  
Structural Elements ◽  
Medical Decision Support ◽  
Modeling Methodology ◽  
Useful Signal ◽  
Diagnostic Signs ◽  
Mammographic Examination

The paper considers the formalization of the stages and modeling of the mammographic examination procedure in the design of medical computer decision support systems. The mammographic examination process is presented in a generalized model, which consists of functional, structural, and mathematical models. The functional model (context diagram) is made using the functional modeling methodology. When analyzing the context diagram, four main functional blocks were identified: register a patient; perform registration and analysis of mammograms; carry out diagnostics; form a survey protocol. If there are standards for maintaining medical records and drawing up examination protocols, the first and last blocks are easily automated. The article focuses on the second and third blocks. At the mammogram analysis stage, the sub-stages “Perform preliminary processing” and “Perform morphological analysis” are essential. Preprocessing of mammograms (adaptive filtering, changing brightness or increasing contrast, etc.) is carried out using digital image processing methods to improve visualization quality. The result of morphological analysis is selecting structural elements and forming a set of diagnostic signs in the form of parameters of the found structural elements. Because some elements of mammograms (microcalcifications) have an irregular structure, specialized morphological analysis methods are used, based on taking into account the features of the images under consideration and their transformation methods in the form of the useful signal, in particular, fractal dimension models. The developed formalized models made it possible to reasonably design the decision support system’s structure during mammographic examinations, information, mathematical, software, and hardware to increase medical services’ efficiency and minimize the risks of medical errors.

Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support

2012 ◽  
pp. 255-272 ◽  
Author(s):  
Jose M. Alonso ◽  
Ciro Castiello ◽  
Marco Lucarelli ◽  
Corrado Mencar
Keyword(s):  
Decision Support ◽  
Fuzzy Rule ◽  
Linguistic Knowledge ◽  
Medical Decision ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Medical Decision Support ◽  
Modeling Methodology ◽  
Stage Renal Disease ◽  
End Stage

Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.

Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support

Data Mining ◽  
2013 ◽  
pp. 1064-1081 ◽  
Author(s):  
Jose M. Alonso ◽  
Ciro Castiello ◽  
Marco Lucarelli ◽  
Corrado Mencar
Keyword(s):  
Decision Support ◽  
Fuzzy Rule ◽  
Linguistic Knowledge ◽  
Medical Decision ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Medical Decision Support ◽  
Modeling Methodology ◽  
Stage Renal Disease ◽  
End Stage

Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.

The Good, the Bad and the Ugly in App Diagnosis: Outcomes and Implications by Example

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 38-38
Author(s):  
Sorana D. Bolboacă ◽  
◽  
Adriana Elena Bulboacă ◽  
◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision ◽  
Medical Decision ◽  
Normal Body Mass Index ◽  
Disk Herniation ◽  
Scientific Evaluation ◽  
Medical Decision Support ◽  
Lower Back ◽  
Clinical Scenarios ◽  
Medical Decision Support Systems

"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is generally not accompanied by a scientific evaluation of the performances, even if they are freely available or not. Two clinical scenarios were created, and Doctor31 retrieved the diagnosis decisions. First scenario: man, 29 years old, and three symptoms: dysphagia, weight loss (normal body mass index), and tiredness. Second scenario: women, 47 years old with L5-S1 disk herniation, abnormal anti-TPO antibodies, lower back pain (burning sensations), constipation, and tiredness. The outcome possible effects and implications, as well as vulnerabilities induced on the used, are highlighted and discussed. "

Evaluation of Fuzzy Relation Method for Medical Decision Support

2010 ◽  
Vol 36 (1) ◽  
pp. 233-239 ◽  
Author(s):  
Kavishwar Wagholikar ◽  
Sanjeev Mangrulkar ◽  
Ashok Deshpande ◽  
Vijayraghavan Sundararajan
Keyword(s):  
Decision Support ◽  
Fuzzy Relation ◽  
Medical Decision ◽  
Medical Decision Support
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