Speaker "Sadid Hasan" Details Back



Deep Learning for Radiology Text Report Classification


In this talk, I will discuss about our proposed advanced deep learning models for classifying free text radiology reports based on the presence of pulmonary emboli (PE). The models are trained on a subset of Stanford training set (2512 reports) and evaluated on reports collected from four major healthcare centers. Our experiments suggest feasibility of broader usage of neural networks in automated classification of multi-institutional imaging text reports for various applications including evaluation of imaging utilization, imaging yield, and clinical decision support tools.

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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.