Name
Abstract | ||
|---|---|---|
Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient's health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries, and customize the desired degree of linguistic granularity. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/978-1-61499-830-3-629 | Studies in Health Technology and Informatics |
Keywords | DocType | Volume |
Natural Language Processing,Information Storage and Retrieval,Electronic Health Records | Conference | 245 |
ISSN | Citations | PageRank |
0926-9630 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Wu | 1 | 50 | 6.61 |
| Andrew Wen | 2 | 2 | 1.71 |
| Yanshan Wang | 3 | 47 | 19.00 |
| Sijia Liu | 4 | 1 | 1.07 |
| Hongfang Liu | 5 | 1479 | 160.66 |