Format

Send to:

Choose Destination
See comment in PubMed Commons below
Artif Intell Med. 2013 May;58(1):63-72. doi: 10.1016/j.artmed.2013.01.003. Epub 2013 Feb 19.

Missing data in medical databases: impute, delete or classify?

Author information

  • 1Massachusetts Institute of Technology, Engineering Systems Division, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. cismondi@mit.edu

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.

OBJECTIVES:

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.

Copyright © 2013 Elsevier B.V. All rights reserved.

PMID:
23428358
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Write to the Help Desk