Deep learning has emerged as the preferred approach for machine learning with limited or no annotated corpora and hand-crafted features. In addition to the success of convolutional and recurrent neural networks in addressing typical NLP tasks e.g. syntactic parsing or sentiment analysis, integrating “memory” and “attention” in neural networks while leveraging relevant knowledge sources yields results that compare to or outperform the state-of-the-art for analysis of semantically-rich narratives in the clinical domain. My presentation highlights some of our work at the AI Lab in Philips Research NA in which we implement attention-based and memory networks to perform clinical paraphrasing and diagnostic reasoning.
Oladimeji (Dimeji) Farri received his PhD in Health Informatics from the University of Minnesota, and MBBS (Medicine and Surgery) from the University of Ibadan, Nigeria, in 2012 and 2005 respectively. He is currently a Senior Research Scientist at Philips Research – North America (PRNA) in Cambridge, Massachusetts, where he leads the Artificial Intelligence (AI) Lab. His interests are in clinical NLP, text analysis, question answering and dialog systems to address medical dilemmas experienced by patients/consumers and healthcare providers. His recent work includes the use of deep learning in offering solutions for clinical decision support and patient engagement.