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：本文使用规则搜索技术，旨在早期识别有可能成为长期或慢性无家可归者的应急无家可归者。使用一家北美主要收容所的数据集，该数据集包含逾40,000名个体12年的服务交互记录。优化的无序搜索剪枝算法(OPUS)用于开发直观且有效的规则，其背景是支持性住房计划的实时传递框架，旨在将高风险客户过渡到此种住房。结果表明，当本文所介绍的方法被应用时，面对长期无家可归这一风险，客户识别的中位数时间从297天降至162天。