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Enhancing Patient Safety Event Reporting by K-nearest Neighbor Classifier
| Content Provider | Semantic Scholar |
|---|---|
| Author | Liang, Chen Gong, Yang |
| Copyright Year | 2015 |
| Abstract | Data quality was placed as a major reason for the low utility of patient safety event reporting systems. A pressing need in improving data quality has advanced recent research focus in data entry associated with human factors. The debate on structured data entry or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The present study suggested a text classification method, k-nearest neighbor (KNN), for predicting subject categories as in our proposed reporting system. Our results demonstrated the feasibility of KNN classifier used for text classification and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events. |
| Starting Page | 93 |
| Ending Page | 99 |
| Page Count | 7 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://wish2014.files.wordpress.com/2014/10/sub-33-paper-wish_paper_v7_submission.pdf |
| Alternate Webpage(s) | https://doi.org/10.3233/978-1-61499-574-6-93 |
| PubMed reference number | 26262533v1 |
| DOI | 10.3233/978-1-61499-574-6-93 |
| Journal | CSHI |
| Volume Number | 218 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Categories Clinical decision support system Data quality Decision Support Systems, Clinical Document classification Human factors and ergonomics Information Systems Information system K-nearest neighbors algorithm Nearest neighbour algorithm Single Linkage Cluster Analysis Text mining Unsupervised learning |
| Content Type | Text |
| Resource Type | Article |
