Patient phenotypic similarity for diagnosis of rare diseases

Speaker: Xiaoyi CHEN

Bio

Xiaoyi Chen is researcher at Institut Imagine, a research institute specialized in genetic diseases. Her research focuses on automated methods to identify rare disease patients in huge real-world-data repositories. She received her PhD in applied mathematics and computational biology at Institut Pasteur, Paris (2015). Between 2016 and 2022, she was a researcher in the Information Sciences to support Personalized Medicine group at Inserm UMR 1138 (now team HeKA Inria-Inserm-Université Paris Cité).

Abstract

Many rare diseases suffer from important delayed- or underdiagnosis issues due to a broad spectrum of phenotypes and high genetic and clinical heterogeneity. One solution to accelerate the diagnosis process is to rely on patients’ electronic health records (EHRs) for automatic phenotyping and develop algorithms to identify from large scale clinical data warehouse patients having similar profiles to those from already diagnosed patients.
In this talk, I will summarize recent efforts in the context of RHU C’IL-LICO project, to develop diagnosis support systems that takes into consideration the semantic relations between clinical concepts and the different levels of relevance presented in patients’ EHRs – including incompleteness, inaccurate phenotyping, noisy phenotypes related to multiple comorbidities and medical histories, as well as the clinical heterogeneity of complex rare diseases and the important imbalance issues.