Clinical time series prediction: Toward a hierarchical dynamical system framework

Zitao Liu, Milos Hauskrecht
2015 Artificial Intelligence in Medicine  
Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical
more » ... ystem framework for Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance.
doi:10.1016/j.artmed.2014.10.005 pmid:25534671 pmcid:PMC4422790 fatcat:2c3mq5hajfac5m3f4o6iq633yu