title: Use of models in identification and prediction of physiology in critically ill surgical patients.
abstract: BACKGROUND
  With higher-throughput data acquisition and processing, increasing computational power, and advancing computer and mathematical techniques, modelling of clinical and biological data is advancing rapidly. Although exciting, the goal of recreating or surpassing in silico the clinical insight of the experienced clinician remains difficult. Advances toward this goal and a brief overview of various modelling and statistical techniques constitute the purpose of this review.
  
  
  METHODS
  A review of the literature and experience with models and physiological state representation and prediction after injury was undertaken.
  
  
  RESULTS
  A brief overview of models and the thinking behind their use for surgeons new to the field is presented, including an introduction to visualization and modelling work in surgical care, discussion of state identification and prediction, discussion of causal inference statistical approaches, and a brief introduction to new vital signs and waveform analysis.
  
  
  CONCLUSIONS
  Modelling in surgical critical care can provide a useful adjunct to traditional reductionist biological and clinical analysis. Ultimately the goal is to model computationally the clinical acumen of the experienced clinician.
authors.affiliation: Department of Surgery, University of California San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Ward 3A, San Francisco, California 94110, USA. mcohen@sfghsurg.ucsf.edu
authors.forename: M J
authors.initials: MJ
authors.lastname: Cohen
mesh_terms: D003422:Critical Care / Q000379:methods*; D016638:Critical Illness / Q000628:therapy*; D006801:Humans; D008954:Models, Biological; D010829:Physiological Phenomena; D013514:Surgical Procedures, Operative