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.
Validating biomedical and clinical data via an annotations based framework: experiences within the PON VQA project
Puccio B.;Lomoio U.;Giancotti R.;Tradigo G.;Guzzi P. H.;Veltri P.;Vizza P.
2023-01-01
Abstract
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.File in questo prodotto:
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