Natural Language Processing for Healthcare
With recent advancements in Deep Learning followed by successful deployment in natural language processing (NLP) applications such as language understanding, modeling, and translation, the general hope was to achieve yet another success in healthcare domain. Given the vast amount of healthcare data captured in Electronic Medical Records (EMR) in an unstructured fashion, there is an immediate high demand for NLP to facilitate automatic extraction and structuring of clinical data for decision support. Nevertheless, the performance of off-the-shelf NLP on healthcare data has been disappointing. Recently, tremendous efforts have been dedicated by NLP research pioneers to adapt general language NLP for healthcare domain. This talk aims to review current challenges researchers face, and furthermore, reviews some of the most recent success stories.
Amir Tahmasebi is the director of machine learning and AI at CODAMETRIX, Boston, MA. He is also a lecturer in Electrical and Computer Engineering Department at Northeastern University, Boston, MA. Prior to joining CODAMETRIX, Dr. Tahmasebi was a Principal R&D Engineer at Disease Management Solutions Business of Philips HealthTech, Cambridge, MA. Dr. Tahmasebi’s research is focused on patient clinical context extraction and modeling through image analysis and Natural Language Processing, outcome analytics and clinical decision support. Dr. Tahmasebi received his PhD degree in Computer Science from the School of Computing, Queen's University, Kingston, Canada. He is the recipient of the IEEE Best PhD Thesis award and Tanenbaum Post-doctoral Research Fellowship award. He has been serving as an industrial Chair for IPCAI conference since 2015. Dr. Tahmasebi has published and presented his work in a number of conferences and journals including AMIA, JDI, IEEE TMI, IEEE TBME, MICCAI, IPCAI, HBM, SPIE, RSNA, and SIIM. He has also been granted more than 10 patent awards.