Abstract
We present the application of ”meta-search engine” in evidence-based medicine (EBM) by online cluster analysis of PubMed literature. The methodology would be especially beneficial to ”evidence-based plastic surgery”. In addition to the initial aim of splitting the diverse documents into organized relevant and homogeneous document groups (clusters), knowledge discovery of new or neglected sub-topics and selection of key phrases based on information technology are stressed.
PubMed query on ”breast reconstruction” in the recent 2 years yielded 496 articles, which was processed by the online tool. Nine larger clusters comprise of about 70% of the literature, which is ”skin-sparing”, ”tissue expander”, ”DIEP flap”, ”nipple”, ”latissimus dorsi”, ”perforator flap”, ”TRAM flap”, and ”internal mammary vessels”. The details of the knowledge structure were further explored, and a new finding of the ”Chinese women” cluster was described.
PubMed query on ”breast reconstruction” in the recent 2 years yielded 496 articles, which was processed by the online tool. Nine larger clusters comprise of about 70% of the literature, which is ”skin-sparing”, ”tissue expander”, ”DIEP flap”, ”nipple”, ”latissimus dorsi”, ”perforator flap”, ”TRAM flap”, and ”internal mammary vessels”. The details of the knowledge structure were further explored, and a new finding of the ”Chinese women” cluster was described.
| Translated title of the contribution | 以線上叢集分析協助“實證整形外科”探索乳房重建文獻的知識架構 |
|---|---|
| Original language | English |
| Pages (from-to) | 303-315 |
| Number of pages | 13 |
| Journal | 中華民國整形外科醫學會雜誌 |
| Volume | 14 |
| Issue number | 4 |
| Publication status | Published - 2005 |
Keywords
- information retrieval IR
- meta-search engine
- cluster analysis
- data mining
- knowledge discovery
- breast reconstruction