Research in Computer Science for Health
Data warehouses and clinical research informatics

Research in computer science applied to the construction and operation of health data warehouses for healthcare and clinical research

Decision support, e-health

Research in computer science applied to the development of decision-support methods and tools for care, management, public health and health research

Drug Modeling – Pharmaceutical knowledge – Prescription – Pharmacovigilance

Research into the formal representation of drugs and their properties, dedicated to prescription assistance, and computer research dedicated to pharmacovigilance.

Research in Computer Science and Health Informatics

Computer science research with some contributions that may be specific to the healthcare field, but not exclusively

previous arrowprevious arrow
next arrownext arrow

Limics, a public research lab in computer science for health, is an interdisciplinary research unit, specialized in computer science and medical informatics, funded by Inserm, Sorbonne University and Université Sorbonne Paris-Nord. We develop innovative approaches to health information processing on both methodological and application levels.

LIMICS is composed of researchers in computer science, as well as universitary physicians and pharmacists associated with services of the Greater Paris University Hospital (AP-HP), the university hospitals of Rouen and Saint-Étienne. This diversity within the same research unit allows multiple collaborations for the development and evaluation of research products. This plurality allows us to develop both applied and methodological research axes.

Our first topic is research in computer science and computer science for health, with fundamental work on knowledge engineering, visualization, natural language processing, machine learning, decision support systems, or the optimization of access to medical knowledge through bibliometric approaches.

In terms of applied research, we work on three main topics:

Drug representation: pharmaceutical knowledge, prescription, pharmacovigilance. This includes work on drug modeling, prescribing and deprescribing, pharmacovigilance, using, for example, data from cohorts or social networks.

Clinical Data Warehouses and cohorts, secondary use of health data. This topic includes work on information extraction, data quality, and real-world clinical trials. The notion of cohort can appear as a result (patient selection from a data warehouse, comparison of similar patients) or as a source of data.

Decision support for care, management, public health and healthcare research. This topic includes both symbolic methods (reasoning, decision support systems) and numerical methods (machine learning) applied to the improvement of decision-making processes for prevention, diagnosis, prognosis, therapy, and patient follow-up. The laboratory’s projects may concern structured clinical data, text (natural language processing), images or knowledge from all types of data. All medical specialties can be concerned, whether for care, management or research.

We also carry out transversal actions on training (in computer science and digital health), service and valorization, ethics and sustainable development.