title: Reducing unnecessary lab testing in the ICU with artificial intelligence.
abstract: OBJECTIVE
  To reduce unnecessary lab testing by predicting when a proposed future lab test is likely to contribute information gain and thereby influence clinical management in patients with gastrointestinal bleeding. Recent studies have demonstrated that frequent laboratory testing does not necessarily relate to better outcomes.
  
  
  METHODS
  Data preprocessing, feature selection, and classification were performed and an artificial intelligence tool, fuzzy modeling, was used to identify lab tests that do not contribute an information gain. There were 11 input variables in total. Ten of these were derived from bedside monitor trends heart rate, oxygen saturation, respiratory rate, temperature, blood pressure, and urine collections, as well as infusion products and transfusions. The final input variable was a previous value from one of the eight lab tests being predicted: calcium, PTT, hematocrit, fibrinogen, lactate, platelets, INR and hemoglobin. The outcome for each test was a binary framework defining whether a test result contributed information gain or not.
  
  
  METHODS
  Predictive modeling was applied to recognize unnecessary lab tests in a real world ICU database extract comprising 746 patients with gastrointestinal bleeding.
  
  
  RESULTS
  Classification accuracy of necessary and unnecessary lab tests of greater than 80% was achieved for all eight lab tests. Sensitivity and specificity were satisfactory for all the outcomes. An average reduction of 50% of the lab tests was obtained. This is an improvement from previously reported similar studies with average performance 37% by [1-3].
  
  
  CONCLUSIONS
  Reducing frequent lab testing and the potential clinical and financial implications are an important issue in intensive care. In this work we present an artificial intelligence method to predict the benefit of proposed future laboratory tests. Using ICU data from 746 patients with gastrointestinal bleeding, and eleven measurements, we demonstrate high accuracy in predicting the likely information to be gained from proposed future lab testing for eight common GI related lab tests. Future work will explore applications of this approach to a range of underlying medical conditions and laboratory tests.
authors.affiliation: Massachusetts Institute of Technology, Engineering Systems Division, 77 Massachusetts Avenue, 02139 Cambridge, MA, USA. cismondi@mit.edu
authors.forename: F
authors.initials: F
authors.lastname: Cismondi
authors.forename: L A
authors.initials: LA
authors.lastname: Celi
authors.forename: A S
authors.initials: AS
authors.lastname: Fialho
authors.forename: S M
authors.initials: SM
authors.lastname: Vieira
authors.forename: S R
authors.initials: SR
authors.lastname: Reti
authors.forename: J M C
authors.initials: JM
authors.lastname: Sousa
authors.forename: S N
authors.initials: SN
authors.lastname: Finkelstein
mesh_terms: D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D018660:Blood Pressure Monitoring, Ambulatory; D001803:Blood Transfusion; D005260:Female; D006471:Gastrointestinal Hemorrhage / Q000175:diagnosis*; D006339:Heart Rate; D006801:Humans; D007362:Intensive Care Units / Q000592:standards*; D007753:Laboratories / Q000592:standards*; D008297:Male; D015233:Models, Statistical; D010100:Oxygen / Q000032:analysis; D011237:Predictive Value of Tests; D012119:Respiration; D012680:Sensitivity and Specificity; D013696:Temperature
chemical_list: D010100:Oxygen