Pharmacoeconomics research identifies, measures, and compares the costs (resources consumed) and consequences of medical products and services, where at least one of the compared alternatives is pharmacotherapy. Pharmacoeconomics has been designed to enable the decision maker to identify the preferred choice among existing alternatives. The decisions are often important for the patients' lives on the one hand, and for payers on the other (where the payer is understood as the institution responsible for financial resources allocation). One of the most commonly used types of pharmacoeconomic analysis is cost-effectiveness analysis. Two different alternatives can be compared using cost-effectiveness analysis if only their medical and clinical consequences could be measured in similar units (clinical or physical parameters). The aim of the project is to use an artificial neural network (ANN) for medical effect prediction, which could help in the extrapolation of pharmacoeconomic analysis' results. To depict neural data analysis tools, a database containing 100 non-small cell lung cancer (NSCLC) patients in non-operative IIIB and IV stage has been used. Each patient was described using 30 factors (i.e. sex, age, anticancer drugs dosage) and, as an output value, the expected survival time was established. The role of the ANN system was to predict the patient's survival time based on the above mentioned information. Binary values were tested as outcomes. Positive values (coded as 1) meant that patient survival time would be equal to or longer than 35 weeks. Negative values (coded as 0) meant that the patient survival time would be shorter than 35 weeks. Binary values were obtained using a threshold, which based on the mean survival time of patients derived from literature. Back-propagation as well as fuzzy-logic neural networks were applied. A 10-fold cross validation method was used to obtain the appropriate models. Final results were compared with the generic, logistic regression-based model. The best prediction score of the ANN model was 82%; higher than logistic regression prediction rate.