title: NLP-PIER: A Scalable Natural Language Processing, Indexing, and Searching Architecture for Clinical Notes.
abstract: Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design. We present architectural best practices as lessons learned in the design and implementation NLP-PIER (Patient Information Extraction for Research), a scalable, extensible, and secure system for processing, indexing, and searching clinical notes at the University of Minnesota.
authors: Academic Health Center-Information Systems.
authors: Reed
authors: R
authors: McEwan
authors: Institute for Health Informatics; Medical School.
authors: Genevieve B
authors: GB
authors: Melton
authors: College of Pharmacy, University of Minnesota, Minneapolis, MN.
authors: Benjamin C
authors: BC
authors: Knoll
authors: Institute for Health Informatics.
authors: Yan
authors: Y
authors: Wang
authors: Institute for Health Informatics.
authors: Gretchen
authors: G
authors: Hultman
authors: Academic Health Center-Information Systems.
authors: Justin L
authors: JL
authors: Dale
authors: Academic Health Center-Information Systems.
authors: Tim
authors: T
authors: Meyer
authors: Institute for Health Informatics; College of Pharmacy, University of Minnesota, Minneapolis, MN.
authors: Serguei V
authors: SV
authors: Pakhomov