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：本文采用规则搜索技术，以早期识别有成为长期或慢性避难所使用者风险的紧急避难所求助者。利用北美某大型避难所的数据集（包含12年间超过4万名个体的服务交互记录），采用无序搜索的优化剪枝（OPUS）算法构建兼具直观性与有效性的规则。这些规则在符合住房计划实时交付需求的框架内进行评估——该住房计划旨在将高风险求助者转介至支持性住房。结果表明，应用本文方法后，识别有慢性避难所使用风险求助者的中位时间从297天降低至162天。