Clinical Natural Language Processing with Deep Learning

Humans use language as a way to express their feelings and communicate with each other. Due to the rapid growth of unstructured electronic data over the last decade, Natural Language Processing (NLP) has become one of the most important technologies of this information age. Similarly, the ever-increasing amount of Electronic Health Record (EHR) clinical free text documents has urged the need to build novel clinical NLP solutions towards optimizing the patient outcomes across the care continuum. Recently, Deep Learning (DL) techniques have demonstrated superior performance over the traditional Machine Learning (ML) techniques for general domain NLP tasks. By contrast, this talk will focus on the clinical domain and present a brief overview of various DL-driven clinical NLP algorithms developed in the Artificial Intelligence lab at Philips Research - such as diagnostic inferencing from unstructured clinical narratives, clinical paraphrase generation, and medical image caption generation.

Dr. Sadid Hasan is a Senior Scientist and Technical Lead of the Artificial Intelligence Group at Philips Research, Cambridge, MA. His recent research focuses on various Natural Language Processing (NLP) problems related to Clinical Information Extraction, Text Classification, Natural Language Inference, Clinical Text Summarization, and Paraphrase Generation using Deep Learning. Prior to joining Philips, Sadid was a Post Doctoral Fellow at the Department of Mathematics and Computer Science, University of Lethbridge, Canada, from where he also obtained his PhD. in Computer Science with a focus in NLP and Machine Learning. Sadid has over 60 peer-reviewed publications in the top NLP/Machine Learning venues, where he also regularly serves as a program committee member/area chair including ACL, IJCAI, EMNLP, NeurIPS, ICML, COLING, NAACL, AMIA, MLHC, MEDINFO, ICLR, ClinicalNLP, TKDE, JAIR etc.