Name
Title | ||
|---|---|---|
Automated mutual exclusion rules discovery for structured observational codes in echocardiography reporting. |
Abstract | ||
|---|---|---|
Structured reporting in medicine has been argued to support and enhance machine-assisted processing and communication of pertinent information. Retrospective studies showed that structured echocardiography reports, constructed through point-and-click selection of finding codes (FCs), contain pair-wise contradictory FCs (e.g., "No tricuspid regurgitation" and "Severe regurgitation") downgrading report quality and reliability thereof. In a prospective study, contradictions were detected automatically using an extensive rule set that encodes mutual exclusion patterns between FCs. Rules creation is a labor and knowledge-intensive task that could benefit from automation. We propose a machine-learning approach to discover mutual exclusion rules in a corpus of 101,211 structured echocardiography reports through semantic and statistical analysis. Ground truth is derived from the extensive prospectively evaluated rule set. On the unseen test set, F-measure (0.439) and above-chance level AUC (0.885) show that our approach can potentially support the manual rules creation process. Our methods discovered previously unknown rules per expert review. |
Year | Venue | Field |
|---|---|---|
2015 | AMIA | Data mining,Observational study,Structured reporting,Computer science,Automation,Ground truth,Artificial intelligence,Natural language processing,Mutual exclusion,Statistical analysis,Test set |
DocType | Volume | Citations |
Conference | 2015 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas A. Forsberg | 1 | 0 | 0.34 |
| Merlijn Sevenster | 2 | 98 | 13.33 |
| Szymon Bieganski | 3 | 1 | 1.06 |
| Puran Bhagat | 4 | 0 | 0.34 |
| Melvin Kanasseril | 5 | 0 | 0.34 |
| Yugang Jia | 6 | 38 | 5.04 |
| Kirk Spencer | 7 | 0 | 1.01 |