Deep Learning for de-identification of clinical documents

Details

Supervisors

Jodogne, Sébastien

Faculty

Ecole polytechnique de Louvain

Degree label

Master [120] en sciences informatiques, à finalité spécialisée

Abstract

The amount of unstructured medical documents increases each year, presenting an opportunity to extract valuable insights that could significantly improve healthcare. However, to take advantage of this potential, it is crucial to de-identify these documents in order to protect patient privacy and to be able to use these documents for research. This study will explore the different deep learning solutions for the de-identification of clinical documents. The first part explores the current strategies to recognize specific words in documents to understand which method has the greatest impact on performances. This evaluation helps to identify the strengths and weaknesses that traditional deep learning approaches may encounter. The second part will introduce an innovative open-source tool: the Incremental Learning Annotator (ILA). This tool enhances the ability to obtain quickly a robust model that achieves good performance. This solves the need of large and well annotated dataset to obtain a robust deep learning model.