title: Missing data in medical databases: impute, delete or classify?
abstract: BACKGROUND
  The multiplicity of information sources for data acquisition in modern intensive care units (ICUs) makes the resulting databases particularly susceptible to missing data. Missing data can significantly affect the performance of predictive risk modeling, an important technique for developing medical guidelines. The two most commonly used strategies for managing missing data are to impute or delete values, and the former can cause bias, while the later can cause both bias and loss of statistical power.
  
  
  OBJECTIVE
  In this paper we present a new approach for managing missing data in ICU databases in order to improve overall modeling performance.
  
  
  METHODS
  We use a statistical classifier followed by fuzzy modeling to more accurately determine which missing data should be imputed and which should not. We firstly develop a simulation test bed to evaluate performance, and then translate that knowledge using exactly the same database as previously published work by [13].
  
  
  RESULTS
  In this work, test beds resulted in datasets with missing data ranging 10-50%. Using this new approach to missing data we are able to significantly improve modeling performance parameters such as accuracy of classifications by an 11%, sensitivity by 13%, and specificity by 10%, including also area under the receiver-operator curve (AUC) improvement of up to 13%.
  
  
  CONCLUSIONS
  In this work, we improve modeling performance in a simulated test bed, and then confirm improved performance replicating previously published work by using the proposed approach for missing data classification. We offer this new method to other researchers who wish to improve predictive risk modeling performance in the ICU through advanced missing data management.
authors: Massachusetts Institute of Technology, Engineering Systems Division, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. cismondi@mit.edu
authors: Federico
authors: F
authors: Cismondi
authors: André S
authors: AS
authors: Fialho
authors: Susana M
authors: SM
authors: Vieira
authors: Shane R
authors: SR
authors: Reti
authors: João M C
authors: JM
authors: Sousa
authors: Stan N
authors: SN
authors: Finkelstein
mesh_terms: D016208:Databases, Factual / Q000592:standards / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D015233:Models, Statistical; D012372:ROC Curve