Data Science and Natural Language Processing to Extract Information from Clinical Narratives

Abstract

In the past decade, a growing adoption of electronic health record (EHR) systems have made massive amounts of clinical narrative data available in computable form. However, extracting relevant information from clinical narratives remains challenging. Clinical notes often contain abbreviations, medical terms, and other jargon that are easy for health professionals, but challenging for automated approaches to disambiguate. Many EHR systems use non-standard document structures to record critical information about medications, diagnoses, and potential complications. Finally, clinical narratives contain sensitive patient information, which raises privacy and security concerns. Data science and natural language processing (NLP) methods, including the recently popular deep learning-based approaches, can unlock information from narrative text and have received great attention in the medical domain. Many clinical NLP methods based on deep learning models have shown promising results in various information extraction tasks. These methods and tools have also been successfully applied to facilitate clinical research, as well as to support healthcare applications. In this tutorial, we will highlight some methods, tools, and technologies to identify medical concepts and entities in clinical text. Deriving from examples in cohort selection, medication extraction, and de-identification of protected health information, the tutorial presenters will lead a hands-on exercise to develop an NLP pipeline for clinical information extraction. The tutorial will spotlight state-of-the-art approaches with domain examples from multiple clinical domains.