Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76507
Title: Association patterns of ontological features signify electronic health records in liver cancer
Authors: Chan, LWC 
Wong, SCC 
Chiau, CC
Chan, TM
Tao, L
Feng, JH
Chiu, KWH
Issue Date: 2017
Publisher: Hindawi Publishing Corporation
Source: Journal of healthcare engineering, 2017, 6493016 How to cite?
Journal: Journal of healthcare engineering 
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.
URI: http://hdl.handle.net/10397/76507
ISSN: 2040-2295
EISSN: 2040-2309
DOI: 10.1155/2017/6493016
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