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UMLSKS SUGGEST: an auto-complete feature for the UMLSKS interface using AJAX
Anantha Bangalore1, Allen Browne, Guy Divita
1US National Library of Medicine, Bethesda, Maryland, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 24, 2007
Summary
This study presents a method for creating auto-complete suggestions for the UMLS Knowledge Source Server (UMLSKS) interface. The approach enables web applications to offer efficient search suggestions without requiring extensive server infrastructure.
Area of Science:
- Computer Science
- Information Science
- Human-Computer Interaction
Background:
- Auto-complete features enhance web interface usability by suggesting user queries.
- Browser implementations typically use client-side caching of past queries.
- Google Suggest offers a search-specific auto-complete but requires significant server resources.
Purpose of the Study:
- To describe a methodology for generating auto-complete suggestions for the UMLS Knowledge Source Server (UMLSKS) interface.
- To provide a scalable solution for implementing auto-complete features in web applications with limited server infrastructure.
Main Methods:
- Developed a methodology to create a curated list of the most likely suggestions for a given application.
- Applied this methodology to generate suggestions for the UMLSKS interface.
Main Results:
- Successfully created a list of relevant suggestions for the UMLSKS interface.
- The proposed methodology can be adapted for various web-based applications.
Conclusions:
- The described methodology offers an efficient way to implement auto-complete functionality.
- This approach reduces the need for large server infrastructures for auto-complete features.
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