Challenges in Understanding Clinical Notes: Why NLP Engines Fall Short and Where Background Knowledge Can Help
Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.
Digital Object Identifier (DOI)
Published in Proceedings of the 2013 International Workshop on Data Management & Analytics for Healthcare, 2013.
© DARE '13, ACM, Inc.
Perera, S., Sheth, A. P., Thirunarayan, K., Nair, S., & Shah, N. (2013). Challenges in Understanding Clinical Notes: Why NLP Engines Fall Short and Where Background Knowledge Can Help. Proceedings of the 2013 International Workshop on Data Management & Analytics for Healthcare, 21-26.