Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

Authors

  • Zitao Liu University of Pittsburgh
  • Milos Hauskrecht University of Pittsburgh

DOI:

https://doi.org/10.1609/aaai.v30i1.10181

Abstract

Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate that our approach is superior on the prediction tasks for multivariate, irregularly sampled clinical time series, and it outperforms both the population based and patient-specific time series prediction models in terms of prediction accuracy.

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Published

2016-02-21

How to Cite

Liu, Z., & Hauskrecht, M. (2016). Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10181

Issue

Section

Technical Papers: Machine Learning Applications