Multivariate Sequential Analytics for Treatment Trajectory Forecasting
Article No.: 20, Pages 1 - 15
Abstract
Chronic conditions, especially cardiovascular disease account for a large burden on modern healthcare systems. These conditions are by their nature ones that unfold over a long period of time, typically involving many healthcare events, treatments and changes of patient status. The gold standard in public health informatics for risk assessment is regression-based. While these techniques are effective in identifying factors contributing to risk, they produce reductive scores (e.g. probability of a specific class of event, like a heart attack) or binary prediction results, and moreover, they are sequence agnostic. In the area of long-term chronic disease management, multivariate sequential modeling offers an opportunity to forecast disease progression and treatment trajectory in a fine-grained manner in order to aid clinical decision making. This paper investigates the suitability of Long short-term memory, a type of recurrent neural network, in conducting multivariate sequential modeling in the healthcare domain, specifically in the task of forecasting. The eventual goal is to apply this technique to linked New Zealand health data through the Vascular Informatics using Epidemiology and the Web (VIEW) research project. This paper presents initial experiments and results for modeling patients' treatment trajectories during hospitalization using the Medical Information Mart for Intensive Care (MIMIC-III) data set.
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Index Terms
- Multivariate Sequential Analytics for Treatment Trajectory Forecasting
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ISBN:9781450366038
DOI:10.1145/3290688
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Published: 29 January 2019
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