Neural network for determining risk rate of post-heart stroke patients


TRENZ Oldřich SEPŠI Milan KONEČNÝ Vladimír

Year of publication 2014
Type Article in Periodical
Magazine / Source Acta Universitatis agriculturae et Silviculturae Mendelianae Brunensis
MU Faculty or unit

Faculty of Medicine

Field Informatics
Keywords self-learning neural network; risk stratifi cation; myocardial infarction
Description The ischemic heart disease presents an important health problem that aff ects a great part of the population and is the cause of one third of all deaths in the Czech Republic. The availability of data describing the patients’ prognosis enables their further analysis, with the aim of lowering the patients’ risk, by proposing optimum treatment. The main reason for creating the neural network model is not only to automate the process of establishing the risk rate of patients suff ering from ischemic heart disease, but also to adapt it for practical use in clinical conditions. Our aim is to identify especially the specifi c group of risk-rate patients whose well-timed preventive care can improve the quality and prolong the length of their lives. The aim of the paper is to propose a patient-parameter structure, using which we could create a suitable model based on a self-taught neural network. The emphasis is placed on identifying key descriptive parameters (in the form of a reduction of the available descriptive parameters) that are crucial for identifying the required patients, and simultaneously to achieve a portability of the model among individual clinical workplaces (availability of parameters).

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