Clinical Natural Language Processing with Deep Learning

The emergence and proliferation of electronic health record (EHR) systems has incrementally resulted in large volumes of clinical free text documents available across healthcare networks. The huge amount of data inspires research and development focused on novel clinical natural language processing (NLP) solutions to optimize clinical care and improve patient outcomes. In recent years, deep learning techniques have demonstrated superior performance over traditional machine learning (ML) techniques for various general-domain NLP tasks, e.g., language modeling, parts-of-speech (POS) tagging, named entity recognition, paraphrase identification, sentiment analysis, etc. Clinical documents pose unique challenges compared to general-domain text due to the widespread use of acronyms and nonstandard clinical jargons by healthcare providers, inconsistent document structure and organization, and requirement for rigorous de-identification and anonymization to ensure patient data privacy. This tutorial chapter will present an overview of how deep learning techniques can be applied to solve NLP tasks in general, followed by a literature survey of existing deep learning algorithms applied to clinical NLP problems. Finally, we include a description of various deep learning-driven clinical NLP applications developed at the artificial intelligence (AI) lab in Philips Research in recent years—such as diagnostic inferencing from unstructured clinical narratives, relevant biomedical article retrieval based on clinical case scenarios, clinical paraphrase generation, adverse drug event (ADE) detection from social media, and medical image caption generation.