Project Summary Our program vision is to unravel the information buried in health-related narratives by advancing text-processing methods in a unified way across all the genres of health texts and distributing them through an advanced NLP software platform under solid governance and sustainability. The crosscutting theme is the investigation of methods for health NLP made possible by big data, fused with health knowledge. The underlying theme of this renewal is the development of methods towards generalizable, efficient and knowledge-rich models in the context of modern machine learning techniques, particularly models implementing attention mechanisms and using large unlabeled datasets. There is growing penetration of deep learning approaches in the field of health natural language processing. Our proposal aims to address critical methodological gaps and understudied areas in the current unprecedented fast-paced environment. Therefore, our renewal lays out novel and much needed explorations of health NLP research which we will advance through our specific aims. Our datasets will continue to span the spectrum of health-related data – Electronic Medical Records clinical narrative, patient-authored on- line community posts, and health-related social media. The evaluation of the methods we will develop will be performed on the key clinical tasks of concept extraction, relation extraction, and phenotyping with comparisons to other traditional or deep learning algorithms as baselines. We will demonstrate impact of our methods and tools through several use cases, ranging from clinical point of care to public health, to translational and precision medicine. Finally, we will disseminate our work through community activities to advance the state of the art in health natural language processing.

Project Narrative There is a deluge of health texts. Natural Language Processing (NLP) holds much promise to unravel valuable information from these large data streams with the goal to advance medicine and the wellbeing of patients. We will advance state-of-the-art NLP by designing robust, scalable methods that leverage health big data, demonstrating relevance on high-impact use cases, and disseminating NLP tools for the research community and public at large.