The Champollion Project: Automatic Structuration of Clinical Features from Medical Records

Cancer is one of the leading causes of mortality worldwide and as populations age, the burden is growing. Treating increasing numbers of patients enables us to gather detailed medical records. Databases with exhaustive, high quality structured data are thus an essential resource for cancer researchers and provide invaluable information to clinicians whenever they need to treat their patients. In addition, these databases fuel our data strategy as the cornerstone of our digital healthcare ecosystem and they provide crucial support for the development of Artificial Intelligence-related projects. Feeding such databases and registries requires manual curation to ensure their quality over time. Finding alternatives to manual structuration is essential because around 80% of the relevant clinical information is contained in open text and it is costly to maintain teams of curators given the growing volumes of data generated every year. In this article we describe an Artificial Intelligence system developed at Institut Curie, capable of structuring clinical features from unstructured Electronic Health Records. Our system allows us to structure clinical data with reduced manual labor and with accuracy comparable to that of expert clinicians, empowering our data ecosystem and improving the support we can give to clinicians and researchers.