Name
Abstract | ||
|---|---|---|
With the introduction of comprehensive electronic medical records, all aspects of patient history and care can now be captured in both structured and free-text format. As part of a quality improvement (QI) project conducted at University of Washington (UW), we work on automating the abstraction of various types of data elements including smoking status by using natural language processing and machine learning approaches. In this abstract, we present our preliminary results for smoking status detection. |
Year | Venue | Field |
|---|---|---|
2012 | AMIA | Data science,Abstraction,Computer science,Medical history,Data type,Medical record,Quality management |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Tepper | 1 | 18 | 2.41 |
| Fei Xia | 2 | 159 | 27.20 |
| Meliha Yetisgen-Yildiz | 3 | 328 | 34.25 |