Ontological features of Electronic Health Records reveal distinct association patterns in liver cancer

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Electronic Health Record (EHR) system is not only aimed to provide a digital and structural form of patient records but also support the clinical decision, patient care and patient advice. The EHR database is still an under-explored big data resource that has hosted a large number of cases with complete recovery, good prognosis, reliable diagnostic tests and effective treatments. 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), was 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). Each feature value was further weighted using a systematic PubMed search method. Association levels between every two features in HCC and NAD groups were quantified using Pearson's correlation coefficient. The distribution of association levels in HCC group was compared with that in NAD group. HCC group reveals a distinct association pattern that signifies liver cancer and provides clinical decision support for suspected cases.
Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
PublisherIEEE
Pages1051-1053
Number of pages3
ISBN (Electronic)9781509016105
DOIs
Publication statusPublished - 17 Jan 2017
Event2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
Duration: 15 Dec 201618 Dec 2016

Conference

Conference2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/TerritoryChina
CityShenzhen
Period15/12/1618/12/16

Keywords

  • Cancer Signature
  • Clinical Decision Support
  • Electronic Health Record
  • Hepatocellular Carcinoma
  • Ontology
  • SNOMED

ASJC Scopus subject areas

  • Genetics
  • Medicine (miscellaneous)
  • Genetics(clinical)
  • Biochemistry, medical
  • Biochemistry
  • Molecular Medicine
  • Health Informatics

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