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Health and social care professionals are under increasing pressure to assimilate the ever-growing volume of data from case notes and electronic medical records. In this paper, we propose and evaluate with domain experts a cognitive system for patient-centric care that leverages and combines natural language processing, semantics, and learning from users over time to support care professionals making informed and timely decisions while reducing the burden of interacting with large volumes of unstructured patient notes. We propose methods for highlighting the entities embedded in the unstructured data and providing a personalized view of an individual. We evaluate through a user study and show a consensus between what the domain experts and the system consider relevant and discuss early feedback on the value of our Note Highlights methods to domain experts.
Terminology, Semantics, Unified modeling language, Taxonomy, Electronic medical records, Diseases

V. Lopez et al., "Note Highlights: Surfacing Relevant Concepts from Unstructured Notes for Health Professionals," 2017 IEEE International Conference on Healthcare Informatics (ICHI), Park City, Utah, USA, 2017, pp. 198-207.
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