Personalized Action Rules for Side Effects Object Grouping

• 作者: Ayman Hajja Hakim Touati Jieyan Kuang Zbigniew W. Ras
• 来源期刊: 智能科学国际期刊(英文)
• 学科: 医学
• 关键词: Meta-Actions Action-Rules PERSONALIZATION Optimization GROUPING
• 年，卷（期）: 2013,（1）
• 页码范围: 24-33
• 页数: 10页
• 分类号: R73

摘要

There have been multiple techniques to discover action-rules, but the problem of triggering those rules was left exclusively to domain knowledge and domain experts. When meta-actions are applied on objects to trigger a specific rule, they might as well trigger transitions outside of the target action rule scope. Those additional transitions are called side effects, which could be positive or negative. Negative side effects could be devastating in some domains such as healthcare. In this paper, we strive to reduce those negative side effects by extracting personalized action rules. We proposed three object-grouping schemes with regards to same negative side effects to extract personalized action rules for each object group. We also studied the tinnitus handicap inventory data to apply and compare the three grouping schemes.