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Methods Inf Med 1995; 34(03): 253-258
DOI: 10.1055/s-0038-1634599
Original Article
Schattauer GmbH

Building Clinical Classifiers Using Incomplete Observations – A Neural Network Ensemble for Hepatoma Detection in Patients with Cirrhosis

H. R. Doyle
1   Section of Computational Medicine, University of Pittsburgh, Pittsburgh PA, USA
2   Division of Clinical Transplantation, Pittsburgh Transplantation Institute, University of Pittsburgh, Pittsburgh PA, USA
,
B. Parmanto
3   Department of Information Sciences, University of Pittsburgh, Pittsburgh PA, USA
,
P. W. Munro
3   Department of Information Sciences, University of Pittsburgh, Pittsburgh PA, USA
,
I. R. Marino
2   Division of Clinical Transplantation, Pittsburgh Transplantation Institute, University of Pittsburgh, Pittsburgh PA, USA
,
K. Aldrighetti
2   Division of Clinical Transplantation, Pittsburgh Transplantation Institute, University of Pittsburgh, Pittsburgh PA, USA
,
C. Doria
2   Division of Clinical Transplantation, Pittsburgh Transplantation Institute, University of Pittsburgh, Pittsburgh PA, USA
,
J. McMichael
1   Section of Computational Medicine, University of Pittsburgh, Pittsburgh PA, USA
,
J. J. Fung
2   Division of Clinical Transplantation, Pittsburgh Transplantation Institute, University of Pittsburgh, Pittsburgh PA, USA
› Author AffiliationsAided by Project Grant No. OK 29961 from the National Institute of Health, Bethesda, MD.
Further Information

Publication History

Publication Date:
16 February 2018 (online)

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Abstract:

One objective of liver transplant evaluation is to identify patients that harbor a hepatoma, but standard screening techniques are not sensitive enough. We trained neural network ensembles to predict the presence of hepatoma in patients with cirrhosis, based on information collected at the time of transplant evaluation. Network architecture and training were modified to handle missing observations. Three ensembles were trained: ensemble A using the subset with no missing observations (528 patients); ensemble B using the complete set, which included missing observations (853 patients); and ensemble C using the smaller subset, originally with complete data, but after a fixed number of observations were deleted (i. e., made “missing”). Ensemble performance on testing sets was very good. The areas under the ROC curves were 0.91, 0.89, and 0.90, for ensembles A. B, and C, respectively. Neural networks can successfully perform this classification task, and strategies can be developed that allow use of incomplete observations.

Keywords:

Neural Networks - Missing Data - Diagnosis - Hepatocellular Carcinoma
 

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