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.

翻译：本文运用规则搜索技术，早期识别存在成为长期或慢性收容所使用者风险的紧急收容所客户。基于北美某主要收容所的数据集（包含12年间与40,000多名个体的服务交互记录），采用无序搜索优化剪枝（OPUS）算法生成既直观又有效的规则。这些规则在可兼容实时住房计划实施的框架内进行评估，该计划旨在将高风险客户过渡至支持性住房。结果表明，应用本文方法后，识别具有慢性收容所使用风险客户的中位时间从297天降至162天。