Khaberni - Researchers have developed an innovative computational model capable of simulating the interaction of tumors with the immune system under conditions of chaos and uncertainty.
This breakthrough, prepared by researchers from the Coastal Polytechnic School (ESPOL) in Ecuador, represents a significant step towards precision medicine in cancer treatment. The study's results were published in the journal "Information Science".
The significance of this new model stems from its ability to handle the large variations in patients' immune responses, which is difficult for traditional mathematical models to determine.
The team uses a third type (Type-3) Fuzzy Logic and neural networks to generate "fields of uncertainty" that illustrate multiple possible scenarios for the same treatment, rather than predicting a single pathway.
The model also provides an interpretative understanding of predictions, unlike traditional AI systems based on black boxes, as it maintains the characteristics of chaos and turning points in patient health, enabling doctors to understand the "reason for the prediction" and make precise treatment decisions.
Among the most notable results of the study is the production of visible clinical risk maps, categorizing patients into safe zones and high-risk areas, using simple linguistic rules such as: "If the delay in CD8+ cells' response is high, the risk of relapse is high." This enables doctors to identify patients who need urgent intervention to accelerate the immune response versus those who can be treated with standard therapy.
This technique opens the door to more powerful medical simulators based on interpretable artificial intelligence, transforming complex numbers into clear and actionable diagnoses, supporting precise decision-making in personalized oncology.
