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A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients

arXiv 2205.09883 ↗First posted 19 May 2022cs.CY cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 28 references that are in the map. First posted on arXiv 19 May 2022.

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3 of 28 references.

References · 3

  1. 2020Interpretable Machine Learning Approaches to Prediction of Chronic Homelessnesscited by 4
  2. 2021The Best Thresholds for Rapid Identification of Episodic and Chronic Homeless Shelter Usecited by 1
  3. 2021Predicting Chronic Homelessness: The Importance of Comparing Algorithms using Client Historiescited by 1

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