Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 2017, Article ID 6493016, 9 pages
https://doi.org/10.1155/2017/6493016
Research Article

Association Patterns of Ontological Features Signify Electronic Health Records in Liver Cancer

1Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Hong Kong
2Philips Research China, Shanghai, China
3Department of Diagnostic Radiology, University of Hong Kong, Pok Fu Lam, Hong Kong

Correspondence should be addressed to Lawrence W. C. Chan; kh.ude.uylop@nahc.ihc.gniw

Received 7 April 2017; Accepted 21 May 2017; Published 6 August 2017

Academic Editor: Zhe He

Copyright © 2017 Lawrence W. C. Chan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electronic Health Record (EHR) system enables clinical decision support. In this study, a set of 112 abdominal computed tomography imaging examination reports, consisting of 59 cases of hepatocellular carcinoma (HCC) or liver metastases (so-called HCC group for simplicity) and 53 cases with no abnormality detected (NAD group), were collected from four hospitals in Hong Kong. We extracted terms related to liver cancer from the reports and mapped them to ontological features using Systematized Nomenclature of Medicine (SNOMED) Clinical Terms (CT). The primary predictor panel was formed by these ontological features. Association levels between every two features in the HCC and NAD groups were quantified using Pearson’s correlation coefficient. The HCC group reveals a distinct association pattern that signifies liver cancer and provides clinical decision support for suspected cases, motivating the inclusion of new features to form the augmented predictor panel. Logistic regression analysis with stepwise forward procedure was applied to the primary and augmented predictor sets, respectively. The obtained model with the new features attained 84.7% sensitivity and 88.4% overall accuracy in distinguishing HCC from NAD cases, which were significantly improved when compared with that without the new features.