Grigorash Alexander, O'Neill Siobhan, Bond Raymond, Ramsey Colette, Armour Cherie, Mulvenna Maurice D
School of Computing, Ulster University, Newtownabbey, United Kingdom.
School of Psychology, Ulster University, Coleraine, United Kingdom.
JMIR Ment Health. 2018 Jun 11;5(2):e47. doi: 10.2196/mental.9946.
This paper presents an analysis of call data records pertaining to a telephone helpline in Ireland among individuals seeking mental health and well-being support and among those who are in a suicidal crisis.
The objective of our study was to examine whether rule sets generated from decision tree classification, trained using features derived from callers' several initial calls, could be used to predict what caller type they would become.
Machine learning techniques were applied to the call log data, and five distinct patterns of caller behaviors were revealed, each impacting the helpline capacity in different ways.
The primary findings of this study indicate that a significant model (P<.001) for predicting caller type from call log data obtained from the first 8 calls is possible. This indicates an association between callers' behavior exhibited during initial calls and their behavior over the lifetime of using the service.
These data-driven findings contribute to advanced workload forecasting for operational management of the telephone-based helpline and inform the literature on helpline caller behavior in general.
本文对爱尔兰一条电话求助热线的通话数据记录进行了分析，这些记录来自寻求心理健康和幸福支持的个人以及处于自杀危机中的人。
我们研究的目的是检验从决策树分类生成的规则集是否可用于预测来电者会成为何种类型，该规则集使用从来电者最初几次通话中提取的特征进行训练。
将机器学习技术应用于通话记录数据，揭示了五种不同的来电者行为模式，每种模式对求助热线的能力有不同影响。
本研究的主要发现表明，根据前8次通话获得的通话记录数据预测来电者类型的显著模型（P<.001）是可行的。这表明来电者在最初通话中表现出的行为与其使用该服务期间的行为之间存在关联。
这些数据驱动的发现有助于对基于电话的求助热线进行运营管理的高级工作量预测，并为有关求助热线来电者行为的文献提供参考。
