The temporal inference with the use of ant-based clustering algorithm and flow graphs in the problem of prognosing complications of medical surgical procedures

In the era of a rapidly aging European society, the demand for proven clinical decision support systems, links health observations with medical knowledge in order to assist clinicians in decision making is constantly growing. An increasing problem for this type of systems is not only the size of the processed data sets but also the heterogeneity of these data. Clinical forecasting often requires processing of both numerical data and multi-category data which are temporal. The conducted research has shown that a good solution to this problem may lie in the use of temporal inference, the ant-based clustering algorithm, rough sets, and fuzzy sets. The experiments used a real set of medical data representing cases of a disease that significantly reduces a woman's quality of life. Each case of uterine myoma disease (which affects more than 50% of women over the age of 35) is represented by more than 140 heterogeneous features. An incorrect decision about the type of surgery (thermoablation or surgery) not only affects female fertility but also the high risk of complications. Therefore, the solution discussed in this paper may turn out to be extremely important.