Clinical Natural Language Processing with Deep Learning
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.
Sadid Hasan is a Senior Scientist at the Artificial Intelligence Lab in Philips Research North America, Cambridge, Massachusetts. His recent work involves solving problems related to clinical question answering, paraphrase generation, and medical image caption generation using Deep Learning. Before joining Philips, he 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 Computational Linguistics, Natural Language Processing (NLP), and Machine Learning in 2013.