Name
Abstract | ||
|---|---|---|
Today, social media is increasingly used by patients to openly discuss their health. Mining automatically such data is a challenging task because of the non-structured nature of the text and the use of many abbreviations and the slang terms. Our goal is to use Patient Authored Text to build a French Consumer Health Vocabulary on breast cancer field, by collecting various kinds of non-experts' expressions that are related to their diseases and then compare them to biomedical terms used by health care professionals. We combine several methods of the literature based on linguistic and statistical approaches to extract candidate terms used by non-experts and to link them to expert terms. We use messages extracted from the forum on cancerdusein org and a vocabulary dedicated to breast cancer elaborated by the Institut National Du Cancer. We have built an efficient vocabulary composed of 192 validated relationships and formalized in Simple Knowledge Organization System ontology. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1177/1460458217751014 | HEALTH INFORMATICS JOURNAL |
Keywords | Field | DocType |
consumer health vocabulary,information extraction,ontology,social media,text mining | Health care,Ontology,World Wide Web,Social media,Expression (mathematics),Knowledge management,Information extraction,Simple Knowledge Organization System,Vocabulary,Medicine,Slang | Journal |
Volume | Issue | ISSN |
25.0 | 4.0 | 1460-4582 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mike Donald Tapi Nzali | 1 | 0 | 0.34 |
| Jérome Aze | 2 | 1 | 1.37 |
| Sandra Bringay | 3 | 183 | 34.40 |
| Christian Lavergne | 4 | 20 | 5.26 |
| Caroline Mollevi | 5 | 0 | 0.34 |
| Thomas Optiz | 6 | 0 | 0.34 |