Name
Abstract | ||
|---|---|---|
More and more health websites hire medical experts (physicians, medical students, experienced volunteers, etc.) and indicate explicitly their medical role in order to notify that they provide high-quality answers. However, medical experts may participate in forum discussions even when their role is not officially indicated. Detecting posts written by medical experts facilitates the quick access to posts that have more chances of being correct and informative. The main objective of this work is to learn classification models that can be used to detect posts written by medical experts in any health forum discussions. Two French health forums have been used to discover the best features and methods for this text categorization task. The obtained results confirm that models learned on appropriate websites may be used efficiently on other websites (more than 98% of F1-measure has been obtained using a Random Forest classifier). A study of misclassified posts highlights the participation of medical experts in forum discussions even if their role is not explicitly indicated. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1177/1460458216682356 | HEALTH INFORMATICS JOURNAL |
Keywords | Field | DocType |
author-profiling,health forums,medical expertise,text categorization | Medical education,Knowledge management,Text categorization,Random forest,Medicine | Journal |
Volume | Issue | ISSN |
25.0 | 1.0 | 1460-4582 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amine Abdaoui | 1 | 6 | 4.83 |
| Jérôme Azé | 2 | 73 | 15.66 |
| Sandra Bringay | 3 | 183 | 34.40 |
| Natalia Grabar | 4 | 9 | 7.64 |
| Pascal Poncelet | 5 | 768 | 126.47 |