Optimization of the adaptability of clinical information extraction systems: deep learning and use of feedback propagation techniques.

Large amounts of unstructured medical data (for example clinical notes) are today available, which offers opportunities for optimization of healthcare quality and patient security. Although Natural Language Processing technology already offers great tools and solutions to automate the processing of medical documents, performance of this technology often decreases with changes of the extraction context (medical specialty, hospital, physician's writing style). This project will study the possibility of a scalable NLP engine able to adapt to such new contexts. To reach this goal, we will explore and combine approaches based on deep neural networks, the human-in-the-loop paradigm and persistent learning. The project is a collaboration with LynxCare Clinical Informatics, a medical IT company focusing on promoting access to medical information and reducing administrative costs in hospitals.

Keywords:INFORMATION EXTRACTION, TEXT ANALYSIS, MEDICAL INFORMATICS

Disciplines:Artificial intelligence, Cognitive science and intelligent systems, Linguistics, Theory and methodology of linguistics, Other languages and literary studies

Project type:Collaboration project