一种用于早期识别长期慢性紧急无家可归收容所客户的规则搜索框架
A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients
AI总结：
提出基于OPUS算法的规则搜索框架，利用北美收容所12年数据构建直观有效规则，在实时支持性住房项目场景下将慢性收容所客户识别中位时间从297天缩短至162天。
摘要：
本文使用规则搜索技术，对有成为长期或慢性收容所使用者风险的紧急无家可归收容所客户进行早期识别。研究使用来自北美一家大型收容所的数据集，其中包含与超过40,000人在12年间的服务互动记录，并采用优化剪枝无序搜索（OPUS）算法来制定既直观又有效的规则。这些规则在一个兼容住房项目实时交付的框架中进行评估，该项目旨在将高风险客户转移至支持性住房。结果表明，应用本文方法后，识别有慢性收容所使用风险客户的中位时间从297天降至162天。
英文摘要：
This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major North American shelter containing 12 years of service interactions with over 40,000 individuals, the optimized pruning for unordered search (OPUS) algorithm is used to develop rules that are both intuitive and effective. The rules are evaluated within a framework compatible with the real-time delivery of a housing program meant to transition high risk clients to supportive housing. Results demonstrate that the median time to identification of clients at risk of chronic shelter use drops from 297 days to 162 days when the methods in this paper are applied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
