Aditya Kousik
2018
Detecting Diabetes Risk from Social Media Activity
Dane Bell
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Egoitz Laparra
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Aditya Kousik
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Terron Ishihara
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Mihai Surdeanu
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Stephen Kobourov
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
This work explores the detection of individuals’ risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual’s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).
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