title: Clinical time series prediction: Toward a hierarchical dynamical system framework.
abstract: 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.
  
  
  METHODS
  Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error.
  
  
  RESULTS
  We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computer Science Department, University of Pittsburgh, 210 South Bouquet Street, Pittsburgh, PA 15260, USA. Electronic address: ztliu@cs.pitt.edu.
authors.forename: Zitao
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Computer Science Department, University of Pittsburgh, 210 South Bouquet Street, Pittsburgh, PA 15260, USA.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
mesh_terms: D001772:Blood Cell Count; D006348:Cardiac Surgical Procedures; D000066491:Clinical Decision-Making / Q000379:methods*; D003198:Computer Simulation; D057225:Data Mining; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D016011:Normal Distribution; D011237:Predictive Value of Tests; D013997:Time Factors
keywords: Clinical time series prediction; Gaussian processes; Hierarchical framework; Linear dynamical system