Validating biomedical and clinical data via an annotations based framework: experiences within the PON VQA project

Machine learning (ML) empowered software tools play a key role in assisting and supporting physicians in clinical procedures, diagnosis and follow-up. These tools analyze data extracted by the biomedical instruments to study diseases or effects of drugs on a large population of patients enabling precision and personalized medicine. In this paper, we present the definition and the implementation of a system based on machine learning algorithms to perform semi-automatic features annotation, question answering and data enrichment. The software prototype will is currently tested in a real clinical scenario in the University Hospital.