20 miliardi di euro l’anno: i costi per il cancro in Italia – Prevenzione attiva, la vera arma vincente
Optimizing an AI-based tool for intercepting cancer driver conditions involves a multi-faceted approach. We utilize machine learning algorithms to analyse a wide array of data types, including genomic data, electronic health records, and more. All the data will be cleaned and pre-processed to be used effectively by the HELIXAFE AI algorithms, and feature selection techniques will be used to identify the key pieces of data and make the model more efficient and interpretable. We will use techniques like cross-validation to assess the accuracy of our model and tune its parameters to improve this accuracy. Techniques like SHAP, LIME, or attention mechanisms in neural networks will be used to make our model’s predictions more understandable. We will ensure the tool complies with HIPAA (Health Insurance Portability and Accountability Act) for U.S. market that provides data privacy and security provisions for safeguarding medical information, and with the European General Data Protection Regulation (GDPR). This might involve techniques like differential privacy to protect individual patient’s data.
