The ever-increasing amount of Electronic Health Record (EHR) clinical free text documents has urged the need to build novel clinical Natural Language Processing (NLP) solutions towards optimizing patient outcomes. Deep Learning (DL) techniques have so far demonstrated superior performance over other Machine Learning (ML) approaches for the 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 Director for AI at CVS Health leading the team responsible for AI-enabled clinical care plan initiatives in Aetna. His recent work involves solving problems related to clinical information extraction, paraphrase generation, natural language inference, and clinical question answering using Deep 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.