title: Learning Bayesian networks for clinical time series analysis.
abstract: BACKGROUND
  Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.
  
  
  METHODS
  Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.
  
  
  RESULTS
  The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands; Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, The Netherlands. Electronic address: m.vanderheijden@cs.ru.nl.
authors.forename: Maarten
authors.initials: M
authors.lastname: van der Heijden
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands.
authors.forename: Marina
authors.initials: M
authors.lastname: Velikova
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands; Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands. Electronic address: peterl@cs.ru.nl.
authors.forename: Peter J F
authors.initials: PJ
authors.lastname: Lucas
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008168:Lung / Q000502:physiology; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000379:methods; D011336:Probability; D029424:Pulmonary Disease, Chronic Obstructive / Q000628:therapy*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
keywords: Bayesian networks; Chronic disease management; Chronic obstructive pulmonary disease; Clinical time series; Machine learning; Temporal modelling