Abstract
Online medical discussion forums/question answering sites have become one of the major resources for people to look for healthcare information. These sites typically contain tremendous user-generated content (UGC) that possesses complex domain-specific information in layman’s terms, which is the opposite of formal medical records kept in hospitals (i.e. Electronic Health Record). The goal of this project is to dissect semantics and extract valuable information systematically from UGC composed in unstructured and unconstrained format. We propose an automatic medical content analyzer that takes into account language semantics as well as progression (evolution) of medical events. The preliminary evaluation on the WebMD dataset shows that evolution-based recommendation uncovers broader domain semantic which might be ignored when using word-level or concept-based features.
| Original language | English (US) |
|---|---|
| Title of host publication | Social, Cultural, and Behavioral Modeling - 12th International Conference, SBP-BRiMS 2019, Proceedings |
| Editors | Robert Thomson, Christopher Dancy, Ayaz Hyder, Halil Bisgin |
| Publisher | Springer Verlag |
| Pages | 103-112 |
| Number of pages | 10 |
| ISBN (Print) | 9783030217402 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Externally published | Yes |
| Event | 12th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2019 - Washington D.C., United States Duration: Jul 9 2019 → Jul 12 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11549 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 12th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2019 |
|---|---|
| Country | United States |
| City | Washington D.C. |
| Period | 7/9/19 → 7/12/19 |
Fingerprint
Keywords
- Text processing
- Topic evolution
- User-generated content
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Chronological semantics modeling : A topic evolution approach in online user-generated medical data. / Chung, Cheng Yu; Hsiao, Ihan.
Social, Cultural, and Behavioral Modeling - 12th International Conference, SBP-BRiMS 2019, Proceedings. ed. / Robert Thomson; Christopher Dancy; Ayaz Hyder; Halil Bisgin. Springer Verlag, 2019. p. 103-112 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11549 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Chronological semantics modeling
T2 - A topic evolution approach in online user-generated medical data
AU - Chung, Cheng Yu
AU - Hsiao, Ihan
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Online medical discussion forums/question answering sites have become one of the major resources for people to look for healthcare information. These sites typically contain tremendous user-generated content (UGC) that possesses complex domain-specific information in layman’s terms, which is the opposite of formal medical records kept in hospitals (i.e. Electronic Health Record). The goal of this project is to dissect semantics and extract valuable information systematically from UGC composed in unstructured and unconstrained format. We propose an automatic medical content analyzer that takes into account language semantics as well as progression (evolution) of medical events. The preliminary evaluation on the WebMD dataset shows that evolution-based recommendation uncovers broader domain semantic which might be ignored when using word-level or concept-based features.
AB - Online medical discussion forums/question answering sites have become one of the major resources for people to look for healthcare information. These sites typically contain tremendous user-generated content (UGC) that possesses complex domain-specific information in layman’s terms, which is the opposite of formal medical records kept in hospitals (i.e. Electronic Health Record). The goal of this project is to dissect semantics and extract valuable information systematically from UGC composed in unstructured and unconstrained format. We propose an automatic medical content analyzer that takes into account language semantics as well as progression (evolution) of medical events. The preliminary evaluation on the WebMD dataset shows that evolution-based recommendation uncovers broader domain semantic which might be ignored when using word-level or concept-based features.
KW - Text processing
KW - Topic evolution
KW - User-generated content
UR - http://www.scopus.com/inward/record.url?scp=85068190704&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068190704&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21741-9_11
DO - 10.1007/978-3-030-21741-9_11
M3 - Conference contribution
SN - 9783030217402
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 103
EP - 112
BT - Social, Cultural, and Behavioral Modeling - 12th International Conference, SBP-BRiMS 2019, Proceedings
A2 - Thomson, Robert
A2 - Dancy, Christopher
A2 - Hyder, Ayaz
A2 - Bisgin, Halil
PB - Springer Verlag
ER -