Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | CRI | Data science,World Wide Web,Architecture,Best practice,Incentive,Semantic search,Computer science,Search engine indexing,Information extraction,Big data,Scalability |
DocType | Volume | Citations |
Conference | 2016 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reed McEwan | 1 | 0 | 1.01 |
| G B Melton | 2 | 264 | 45.72 |
| Benjamin C. Knoll | 3 | 0 | 2.03 |
| Yan Wang | 4 | 25 | 10.08 |
| Gretchen M. Hultman | 5 | 0 | 1.69 |
| Justin L. Dale | 6 | 0 | 0.34 |
| Tim Meyer | 7 | 0 | 0.34 |
| Serguei V S Pakhomov | 8 | 471 | 40.62 |