Abstract
The increasing availability of mental health data presents both opportunities and challenges, particularly due to the unstructured and noisy nature of such data. Data mining—an analytical approach for extracting knowledge from large datasets—is becoming increasingly prevalent in the fields of medicine and mental health. By employing data mining techniques, the insights can help inform the development of enhanced digital tools for mental health and fostering a more personalised user experience. One notable method within this domain is association rule mining, which identifies frequent relationships between items in a dataset. This study aims to apply association rule mining to a dataset generated by users of a digital employee wellbeing platform, focusing on the relationships between various tools and resources utilised on the platform. The Inspire Support Hub is a digital employee wellbeing platform featuring tools such as a mood tracker, a chatbot for self-assessments, and psychoeducational resources. User interactions with the platform are logged as anonymous events, including clicks, mood entries, and self-assessment results, each associated with a unique user ID and timestamp. Upon registration, users enter a company pin and their sector is recorded. From February 2019 to April 2023, 11,583 users engaged with the platform over 16,657 sessions. The analysis was conducted using R Studio, employing the dplyr and tidyverse packages for data cleaning and wrangling, along with ggplot2 for visualisation. The event logs were transformed into transaction data for association rule mining using the arules package. The Apriori algorithm was applied with a minimum support threshold of 0.05 and a confidence level of 0.8, ensuring that only rules with at least 80% accuracy were included. Applying association rule mining on the employee wellbeing platform dataset revealed distinct sets of co-associations, with significant emphasis on the chatbot and mood tracker. This is predictable, considering that the iHelpr chatbot, anxiety and stress self-assessments, and mood tracker are the platform’s most frequently used components. The association rules derived from this analysis can offer valuable insights into the user journey, such as discovering frequent usage patterns, and based upon this the platform could recommend personalised features and content tailored to each user's preferences and behaviour.
Original language | English |
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Publication status | Published (in print/issue) - 9 Dec 2024 |
Event | 32nd Irish Conference on Artificial Intelligence and Cognitive Science - University College Dublin, Dublin, Ireland Duration: 9 Dec 2024 → 10 Dec 2024 https://aics2024.ucd.ie/ |
Conference
Conference | 32nd Irish Conference on Artificial Intelligence and Cognitive Science |
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Abbreviated title | AICS |
Country/Territory | Ireland |
City | Dublin |
Period | 9/12/24 → 10/12/24 |
Internet address |
Keywords
- Machine learning
- association rule mining
- employee mental health
- employee wellbeing