使用机器学习的社会服务信息和转介的自动化方法

M. Sharan, N. K. Ottilingam, C. Mattmann, Karanjeet Singh, M. Marin, Amy Latzer, Colin Foon, Umesh Handore
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引用次数: 1

摘要

洛杉矶县信息和转介联合会(211洛杉矶县)是一个全国公认的服务中心,它为那些需要社会服务资源的人提供转介,这些资源在整个洛杉矶县和全国范围内都有,为那些需要帮助的人和高危人群提供服务。目前,转诊是使用基于网络的在线转诊系统进行的,该系统由多年来收集的丰富的高度整理的数据集支持,并由国家社会服务分类提供信息。为了支持我们的在线系统和新网站的资源推荐,我们的研究团队调查并实现了一个自动资源推荐系统,该系统可以从呼叫者的人口统计信息和人类专家收集的历史推荐数据中学习,以便在主动呼叫时推荐站点。该系统利用最先进的多标签神经网络分类器,通过网格搜索进行调整,以获得该系统的最佳超参数。我们创建的自动化方法允许211洛杉矶县交互式地为有需要的人提供有意义的转介。在本文中,我们描述了我们的评估策略和我们的系统在包含超过45万个呼叫的一年数据集上的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automated Approach for Information and Referral of Social Services Using Machine Learning
The Information and Referral Federation of Los Angeles County (211 LA County) is a nationally recognized service center that makes referrals to those in need of social service resources available at sites throughout Los Angeles County and nationally for those in need and for at-risk populations. Referrals are currently made using an on-line web-based referral system backed by a rich highly curated dataset collected over years and informed by a national taxonomy of social services. In support of resource referrals both for our on-line system, and for a new website presence, our research team has investigated and realized an automated resource referral system that learns from a caller's demographic information and historical referral data collected by human experts to recommend sites at the time of an active call. This system leverages a state of art multi-label neural network classifier, tuned by grid search for obtaining the best hyper-parameters for this system. The automated approach we have created allows 211 LA County to interactively provide a meaningful referral to those in need. In this paper, we describe our evaluation strategy and accuracy of our system on a one-year dataset containing over 450 thousand calls.
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