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We apply semi-supervised topic modeling techniques to detect health-related discussions in everyday telephone conversations, which has applications in large-scale epidemiological studies and for clinical interventions for older adults. The privacy requirements associated with utilizing everyday telephone conversations preclude manual annotations; hence, we explore semi-supervised methods in this task. We adopt a semi-supervised version of Latent Dirichlet Allocation (LDA) to guide the learning process. Within this framework, we investigate a strategy to discard irrelevant words in the topic distribution and demonstrate that this strategy improves the average F-score on the in-domain task and an out-of-domain task (Fisher corpus). Our results show that the increase in discussion of health related conversations is statistically associated with actual medical events obtained through weekly selfreports.

Detecting Health Related Discussions in Everyday Telephone Conversations for Studying Medical Events in the Lives of Older Adults