INTEGRATION OF COMPUTER SCIENCE METHODS IN HEALTH CARE TO ANALYZE CLINICAL DATA OF PATIENTS WITH ONCOLOGICAL DISEASES, ALLOWING TO PREDICT THE EFFECTIVENESS OF IMMUNOTHERAPY BASED ON IMMUNE SYSTEM RESPONSE PATTERNS

The integration of computer science methods, particularly Artificial Intelligence (AI) and machine learning, in oncology has shown significant promise in improving the prediction and personalization of cancer treatment. Immunotherapy, a revolutionary treatment for various cancers, relies heavily on the body’s immune response to target and destroy tumor cells. However, predicting the effectiveness of immunotherapy remains a challenge, due to the complex and variable immune system responses among patients. AI-driven predictive models, using large-scale clinical data and immune system response patterns, offer a powerful approach to overcoming these challenges. This paper explores the application of AI and machine learning techniques in analyzing clinical data from oncological patients to predict the success of immunotherapy treatments. Various machine learning models, such as artificial neural networks and decision trees, are used to analyze clinical data, biomarkers, and immune profiles to forecast treatment outcomes. The results demonstrate promising accuracy in predicting immunotherapy efficacy, highlighting the potential of AI to enhance personalized cancer care. This research underscores the importance of integrating advanced computational methods to improve clinical decision-making and outcomes in cancer treatment.