We introduce a machine learning approach to predict chronic homelessness from de-identified client shelter records drawn from a commonly used Canadian homelessness management information system. Using a 30-day time step, a dataset for 6521 individuals was generated. Our model, HIFIS-RNN-MLP, incorporates both static and dynamic features of a client's history to forecast chronic homelessness 6 months into the client's future. The training method was fine-tuned to achieve a high F1-score, giving a desired balance between high recall and precision. Mean recall and precision across 10-fold cross validation were 0.921 and 0.651 respectively. An interpretability method was applied to explain individual predictions and gain insight into the overall factors contributing to chronic homelessness among the population studied. The model achieves state-of-the-art performance and improved stakeholder trust of what is usually a "black box" neural network model through interpretable AI.

中文翻译：

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预测慢性无家可归的可解释机器学习方法

我们引入了一种机器学习方法，通过从常用的加拿大无家可归者管理信息系统中提取的未识别客户收容所记录来预测长期无家可归者。使用 30 天的时间步长，生成了 6521 个人的数据集。我们的模型 HIFIS-RNN-MLP 结合了客户历史的静态和动态特征，以预测客户未来 6 个月内的长期无家可归。训练方法经过微调以实现高 F1 分数，从而在高召回率和精确度之间取得理想的平衡。10 倍交叉验证的平均召回率和准确率分别为 0.921 和 0.651。应用可解释性方法来解释个人预测，并深入了解导致研究人群中长期无家可归的整体因素。