Name
Abstract | ||
|---|---|---|
Clinical notes contain rich information about supplement use that is critical for detecting adverse interactions between supplements and prescribed medications. It is important to know the context in which supplements are mentioned in clinical notes to be able to correctly identify patients that either currently take the supplement or did so in the past. We applied text mining methods to automatically classify supplement use into four status categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). We manually classified 1,300 sentences into these categories, which were further split as training (1000 sentences) and testing (300 sentences) sets. We evaluated the 7 types of feature sets and 5 algorithms, and the best model (SVM with unigram, bigram and indicator word within certain distance) performed F-measure of 0.906, 0.913, 0.914, 0.715 for status C, D, S, U, respectively on the testing set. This study demonstrates the feasibility of using text mining methods to classify supplement use status from clinical notes. |
Year | Venue | Field |
|---|---|---|
2017 | CRI | Family medicine,Medicine |
DocType | Volume | ISSN |
Conference | 2017 | 2153-4063 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yadan Fan | 1 | 4 | 3.16 |
| Lu He | 2 | 0 | 0.34 |
| Serguei V S Pakhomov | 3 | 471 | 40.62 |
| G B Melton | 4 | 264 | 45.72 |
| Rui Zhang | 5 | 45 | 4.92 |