Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

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We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment. and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and u) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN).
Publisher
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
Issue Date
2020-02-07
Language
English
Citation

34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence, pp.3649 - 3657

ISSN
2159-5399
URI
http://hdl.handle.net/10203/288838
Appears in Collection
RIMS Conference Papers
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