Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data

Alexander Grigorash, Siobhan O'Neill, Raymond Bond, Colette Ramsey, Cherie Armour, Maurice Mulvenna

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
108 Downloads (Pure)


Background: 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.Objective: 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.Methods: 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.Results: 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.Conclusions: 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.
Original languageEnglish
Article numbere47
Number of pages14
JournalJMIR Mental Health
Issue number2
Publication statusPublished (in print/issue) - 11 Jun 2018


  • data mining
  • machine learning
  • clustering
  • classification
  • mental health
  • Suicide


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