Natural Language Processing for Healthcare: Current Trends and Road to Future

It is estimated that the healthcare industry will produce 2.3 trillion gigabytes of unstructured data in 2020 alone and the increasing trend will continue with a rate of 48% annually [The NY Times]. With recent advancements in Natural Language Processing (NLP) and the introduction of Transformers and specifically BERT language model, there is a high hope for NLP technologies to facilitate and expedite automatic extraction and structuring of healthcare data for decision support, better diagnosis and outcome for the patient while reducing the risk and the cost. Nevertheless, the utilization of the off-the-shelf NLP on healthcare data has yet to prove its effectiveness and scalability. Recently, tremendous efforts have been dedicated by NLP research pioneers to adapt general language NLP techniques for healthcare-specific domain. This talk aims to review some of the current challenges researchers face, and furthermore, reviews some of the most recent success stories and finally, draws a vision for the future.
Who is this presentation for?
general audience
Prerequisite knowledge:

What you'll learn?
Intro to NLP, Intro to Healthcare applications, review of state-of-the-art NLP

Amir Tahmasebi is the Senior Director of Machine Learning and AI at CODAMETRIX, Boston, MA. Prior to joining CODAMETRIX, Dr. Tahmasebi was a Principal Research Engineer at Disease Management Solutions Business of Philips HealthTech. Dr. Tahmasebi’s research has been focused on patient clinical context extraction and modeling through medical image analysis and Natural Language Processing. Dr. Tahmasebi received his PhD degree in Computer Science from the School of Computing, Queen's University, ON, Canada. He is the recipient of the IEEE Best PhD Thesis award and Tanenbaum Post-doctoral Research Fellowship award. He is a committee member of MICCAI 2020 and 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 NeuroIPS, MICCAI, IPCAI, SPIE, JDI, IEEE TMI, and IEEE TBME. He has also been granted more than 12 patent awards.